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• A novel water transparency calculation algorithm is introduced, employing deep neural network and image processing technology. This algorithm obviates the need for a water gauge, allowing the calculation of water transparency solely through the analysisof Secchi disk videos. Notably, it demonstrates heightened detection efficiency and success rates. • An enhanced Siamese tracker, termed SiamDCFF (Siamese tracker based on dual correlation feature fusion), is proposed for Secchi disk recognition. Distinguishing features of SiamDCFF include a dual correlation module and a feature fusion module. On the Secchi disk dataset, SiamDCFF achieves the highest success rate, outperforming other methods detailed in this paper. • The simplified 3D-ResNet [41] is leveraged to assess the state of the Secchi disk, transforming the critical position determination problem into an optimization challenge. The objective function involves maximizing the number of visible and invisible Secchi disk images on both sides of the critical position. This method facilitates adaptive critical position determination. • A novel Gated Recurrent Unit based on auto-regression (ARGRU) is proposed, taking into account the motion characteristics of the Secchi disk. ARGRU achieves a higher success detection rate for water transparency compared to the water gauge recognition method. Notably, ARGRU outperforms RNN [42], GRU, and LSTM [43] on the dataset presented in this paper. Water transparency is traditionally assessed through visual inspection using a lowered Secchi disk (SD) into the water, with the disappearance depth of the SD recorded as the measure of transparency. However, manual and visually-dependent frequency measurements of the SD render this process labor-intensive and time-consuming. This paper presents a comprehensive machine-vision-based algorithm designed for the automatic computation of water transparency using Secchi disk videos. The algorithm leverages multiple deep neural networks, including an enhanced Siamese tracking model, a 3D-ResNet, and an improved GRU network. Trained models directly identify the SD in the video, calculating water transparency by processing pixel information. Experimental results demonstrate the algorithm's efficacy in estimating water transparency across diverse natural environments, achieving commendable accuracy (MAE = 3.6 cm MSE = 21.5 cm RMSE = 4.6 cm), rapid processing speed (average 6.87 s), and robust stability. In comparison with the former water-gauge-based algorithm, our proposed algorithm exhibits heightened efficiency and a superior detection success rate. [ABSTRACT FROM AUTHOR]

Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to MEF. However, although many efforts have been made on developing MEF algorithms, the lack of benchmarking studies makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus hindering the development of this field significantly. In this paper, we fill this gap by proposing a benchmark of multi-exposure image fusion (MEFB), which consists of a test set of 100 image pairs, a code library of 21 algorithms, 20 evaluation metrics, 2100 fused images, and a software toolkit. To the best of our knowledge, this is the first benchmarking study in the field of MEF. This paper also gives a literature review on MEF methods with a focus on deep learning-based algorithms. Extensive experiments have been conducted using MEFB for comprehensive performance evaluation and for identifying effective algorithms. We expect that MEFB will serve as an effective platform for researchers to compare the performance of MEF algorithms. • A literature review on multi-exposure image fusion is presented. • A multi-exposure image fusion benchmark is proposed. • The benchmark integrates a test set, algorithms, evaluation metrics, and a toolkit. • Comprehensive performance comparison is conducted and presented. [ABSTRACT FROM AUTHOR]

Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the Mask R-CNN algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a Mask R-CNN image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In Mask R-CNN model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of Mask R-CNN model has significant advantages. [ABSTRACT FROM AUTHOR]

This paper proposes the planar image-space trajectory (PIST) planning algorithm for contour following using computer vision and a force control scheme. The PIST algorithm generates machining trajectories offset to a planar workpiece with an applied force direction so that a force controller can be utilized. The PIST algorithm starts with an image of the workpiece, corrects for the perspective of the camera, defines a contour following trajectory in image space, and then transfers the trajectory into the robot's workspace for playback. A case study is presented where the PIST algorithm is applied to deburring sheet metal parts for validation through experimentation. A comparison is performed between the PIST algorithm and CAD/CAM software using CAD data for path design and a laser scanner for localization. The PIST planner achieved a positional accuracy greater than 1.7 mm compared to the CAM/CAM accuracy of 1.2 mm. An admittance controller successfully accounted for the positional error to achieve satisfactory and equivalent surface finish results for both trajectory planning approaches. The PIST planner is a beneficial alternative for trajectory planning as it significantly reduced the setup and process time while adapting to deviations from the design dimensions and manufacturing imperfections. [ABSTRACT FROM AUTHOR]

The paper presents a computationally efficient method developed to automatically detect obstacles that appear at railroad-road crossings. The proposed algorithm works maintenance-free and automatically detects obstacles that appear and stay or are left at the crossing. These obstacles can pose a threat for the trains causing even a trail derailment. When a detected obstacle is a person it is crucial to generate a warning in order to react and safely remove the person before the train arrives. The proposed algorithm is based on live video stream analysis. The standard image processing methods were used in order to detect obstacles in the acquired images. However, several techniques were proposed to obtain the desired results dealing with image vibrations, heavy rain and snow. Such robustness was obtained by building and maintaining a fuzzy background frame and appropriate filtering. The obstacle detection system was implemented and tested. The results confirmed its correct operation and usefulness in real-world conditions. [ABSTRACT FROM AUTHOR]

The analysis of two-dimensional images enables the detection of structures, objects and their further classification. In the Internet of Things, images are data obtained from cameras or projections of signals. In this paper, we propose a hybridization of the classic RANSAC approach with a selected heuristic algorithm like Grey Wolf Optimizer. This solution allows for increasing the ability to adjust images by analyzing the detected key points of the image. Such hybridization is interesting in terms of the possibility of obtaining stitching accuracy. Moreover, we have introduced the automation of parameter selection to increase the accuracy and speed of the real-time algorithm. For this purpose, an analysis of the results of the algorithm for combining video frames into one large image allowed for a significant reduction in the amount of data processing time. The proposed algorithm was tested on various heuristic algorithms, where their differences and impact on the operation of the method were indicated and discussed. Moreover, the proposition was tested in practical application by automatically creating an image based on video frames recorded by the drone. • Hybridization of RANSAC with a heuristic solution for image stitching purposes. • A method of automating the selection of parameters in the algorithm. • Framework for minimizing the number of calculations in smart devices. • Practical application of the proposed method in IoT solution using a drone. [ABSTRACT FROM AUTHOR]

Driving in the wrong direction is one of the main reasons that cause road accidents in Thailand. To efficiently detect wrong direction driving vehicles, we proposed a system that can track those moving vehicles from CCTVs using deep learning and image processing techniques. Our proposed system is composed of two main algorithms: Road Lane Boundary detection from CCTV algorithm (RLB-CCTV) and Majority-Based Correct Direction Detection algorithm (MBCDD). In this paper, we introduced the RLB-CCTV algorithm and the enhanced version of MBCDD algorithms for detecting road boundaries and wrong-direction driving vehicles. Based on our experiments, our system can identify the road lane boundary and track vehicles driving in the wrong direction on each lane with an accuracy of 96.61%. [ABSTRACT FROM AUTHOR]

The method based on machine vision image processing is used to detect the surface defects of Si 3 N 4 bearing roller. Owing to the variety of defects, small area and low contrast, it is easy to miss or error detection. In this paper, an adaptive update template defect enhancement algorithm based on Gaussian model is proposed. First, a large number of surface images of Si 3 N 4 bearing roller are collected to obtain the non-defect background statistical feature, and the background characteristic curve is fitted by Gaussian model. Further, the initial background template is gained according to the Gaussian curve. Then, combined with the gray distribute of defect images and initial background template, unique adaptive update template can be established. Finally, subtraction operation and nonlinear enhancement are used to improve the comparison of defect information and background. Through inverse sorting, adaptive threshold segmentation and Canny operation, the precise positioning of defects is realized. The enhancement algorithm can effectively enhance the contrast and eliminate the influence of noise. The average detection time is 0.84s, and the detection accuracy is 96.2%. [ABSTRACT FROM AUTHOR]

The task of segmenting small infrared targets, which have few pixels and weak features, has been a difficult problem in the field of small target image processing. Small targets not only exist in general images, but also widely exist in drone cameras, communication base station cameras, rescue cameras, and vehicle cameras. Research on small target segmentation algorithm is very important for analyzing and utilizing these images, and it also has important application value in security, traffic, and rescue. Traditional small target segmentation algorithms are able to segment objects with simple target contour edges and large differences in signal strength. The traditional algorithm often has a high false detection rate and missed detection rate when facing several targets with weak signal strength. It does not perform well in complex scenes. In this paper, we introduce an infrared small target segmentation scheme facing multiple types and numbers of targets. This paper also produces an infrared UAV and pedestrian dataset for validation. Experiments show that the optimized algorithm has a better detection effect on targets in complex scenes. [ABSTRACT FROM AUTHOR]

Layout analysis is essential step in information extraction from scanned document images. In this paper we propose an algorithm for aligning layouts generated with different OCR engines. The main requirement is to always generate the same layout for the given document image regardless of OCR engine used for image processing. In that way information extraction from scanned documents, that is heavily dependent on fields positions in the document, does not depend on specific OCR engine. In other words, it is sufficient to maintain universal extractor knowledge and not necessary to train extractor explicitly with samples processed on specific OCR engine. The proposed algorithm can handle administrative documents with complex layout. [ABSTRACT FROM AUTHOR]

The pandemic of Covid-19 has caused a shift of paradigm of education, from face-to-face to e-learning. E-learning leads to an escalation in digitalization of handwritten documents because it requires submission of homework and assignments through online. To help teachers in checking digitalized handwritten homework, this paper proposes an automatic checking system based on a convolutional neural network (CNN) for handwritten numeral recognition. The CNN is used to recognize four arithmetic operations in mathematical questions consisting of addition, deduction, multiplication and division. The performance CNN in handwritten numeral recognition have been optimized in terms of activation function and gradient descent algorithm. The proposed CNN is also trained and tested with the MNIST handwritten data set. The experimental results show that the recognition accuracy the improved CNN improves to a certain extent as compared to before optimization. [ABSTRACT FROM AUTHOR]

• Performance analysis of different video-based eye blinking detection. • Development of a low-cost imaging acquisition system. • Quality metrics analysis of proposed algorithms. • Performance comparison between RGB and Grayscale images. • The red channel of the RGB camera achieves in our experiments a 100 % eye blink detection rate for event detection. The eye is considered a rich source for gathering information on our daily lives. In addition, the necessity to classify the eye as open or closed and detect eye blinks is increasing in various fields. For instance, it has been proven that eye blinks can be related to different ophthalmological conditions. Nevertheless, accurate, and robust blink estimation poses significant challenges to the efficacy of real-time applications due to the variability of eye images and light conditions. In this paper eye blinking has been monitored using a chin rest and an iOS smartphone attached to a cartesian machinery. Five video-based eye blink algorithms to classify frames have been analyzed and compared in terms of performance. To evaluate the results, different widely used statistical metrics have been employed. The results show that the proposed techniques successfully detect eye blinks, whereas the frame classifier shows different scores depending on the algorithm used. [ABSTRACT FROM AUTHOR]

This paper introduces a novel generalized multifrequency fusion method for defect detection in Eddy Current NDE. The proposed technique provides an ability to highlight small defect signals in presence of noise or larger signals from structural irregularities. This paper contains a complete mathematical formulation of the method and its applications to simulated and experimental data arising from two typical ECT problems: detection of the small cracks in the tube wall near tube support region of steam generator tubes, and detection of small cracks in the bottom layer of two-layer aluminum panels riveted with steel fasteners. [ABSTRACT FROM AUTHOR]

Underwater images acquired in scattering environments are generally of poor quality because of the attenuation and backscattering of light when it passes through water with scattering particles. Polarimetric de-scattering methods can be used to significantly enhance the imaging quality in such a so-called turbid water. However, due to the complexity of polarimetric de-scattering algorithms, it is hard to achieve real-time de-scattering output from a polarimetric camera. In this paper, in order to efficiently increase the computational efficiency, the polarimetric de-scattering algorithm is optimized and a multi-threading framework is developed that enables the algorithm to run in real-time on ordinary laptops for the same polarimetric camera. We demonstrate the imaging performance by using the underwater polarimetric de-scattering system we proposed. We analyze the algorithm under different scattering conditions and discuss its optimal parameters. We find that there is a significant increase in the number of SIFT (i.e., scale invariant feature transform) feature points extracted from the processed image compared to the original image, showing that our system has potential applications in pattern recognition, computer vision, etc. [ABSTRACT FROM AUTHOR]

• Adaptive grayscale image correction based on RAW prior knowledge is provided. • Adaptive power correction and compensation is provided. • Adaptive brightness and power correction based on HIS prior knowledge is provided. • An adaptive enhancement algorithm based on prior knowledge and HIS space. • The algorithm can achieve the best performance on various multi-modal data sets. • The best performance in the task of high grayscale infrared image target detection. Gray-scale images are widely used in many fields, including satellite remote sensing, autonomous driving, medical CT, and X-ray detection, but gray-scale images have many shortcomings, such as high bit, low contrast, and high dynamic ranges. The visual analysis of these images is complicated and limited by people's psychology and physiology, and pseudo-color technology is an effective way to solve this problem. To address the weak adaptive capability, poor robustness, and insufficient generalization of existing pseudo-color enhancement methods, an adaptive enhancement algorithm for high grayscale images based on prior knowledge and HIS space, called APKHHIS, was proposed. Firstly, an adaptive gray-scale image correction mapping algorithm based on original prior knowledge was designed, and the quantization of images with different bit depths is realized. Secondly, to ensure the effectiveness of the enhanced data, an adaptive power compensation method was designed to compensate for the brightness of the gray-scale corrected images. Finally, an adaptive brightness and power correction algorithm was innovatively designed for the first time, which can adaptively adjust the color saturation and spatial brightness of the image more reasonably. To verify the effectiveness of the algorithm proposed in this paper, adaptive enhancement experiments were carried out on multi-modal data sets. The subjective and objective experimental results show that the APKHHIS algorithm can achieve the best performance on various data sets, and has strong robustness and universality, which provides an effective solution for the adaptive enhancement of multi-modal low-quality images. [ABSTRACT FROM AUTHOR]

Digital image processing (DIP) has great application values in many fields, especially in remote sensing image processing, which represents the acquisition, enhancement, analysis, encoding, transmission, and storage of remote sensing images. With the development of chip technology and parallel computing technology, various digital image processing technologies have been successfully applied to satellite applications to help researchers exploit reliable information from remote-sensing images. However, the huge amount of images generated by ultra-high resolution optical remote sensing satellites put great pressure on existing transmission, storage, and processing technologies. Therefore, this paper proposes a spatio-temporal compression pipeline for remote sensing images based on lossy compression methods with ultra-high compression ratios to reduce the overhead required for the transmission and storage of remote sensing images while maintaining the quality of the compressed images. The experimental results show that the proposed method outperforms the classical image compression such as JPEG-2000. [Display omitted] [ABSTRACT FROM AUTHOR]

• This paper presents a future prediction technique for using in S.R. measurement. • The suggested method reduces the required data amount used in future prediction. • To obtain a quicker response, the time series analysis is combined with the ICA and MVE algorithms. • The maximum error of suggested method is equal or less than 8% for Ra ≥ 0.4 μm. The momentary control of manufacturing processes is one of the ways to increase the productivity of production lines. The online measurement of the surface roughness by non-contact methods can be utilized in order to predict the future of surface texture and modify the machining parameters. In the technique proposed at this paper, the surface texture is extracted by combining the 2D surface photography and wavelet approach. Then, by extracting the time delay parameters, the embedding dimension and the false nearest neighbor of the produced surface texture, the future surface roughness is predicted. The results show that this technique can be used in lapping, grinding, turning, and milling processes. Although the maximum roughness error occurred in the surface roughness prediction is 24%, the prediction error is almost constant after Ra = 0.4 μm in different machining processes (about 7%). This study is in line with the development of the proposed method by Pour (2018). [ABSTRACT FROM AUTHOR]

The colored paintings on the surfaces of ancient Chinese buildings have suffered from the wind and the sun and produced many defects such as paint loss, blurring, and color distortion. Previous methods have not been able to virtually repair them well. Therefore, this paper proposed a virtual restoration method for the weathered beams in the Forbidden City using multiple deep learning algorithms. Instead of using only one technology to restore paintings, this paper divided the painting into 3 parts, i.e., the background, the golden edges, and the dragon patterns, and restored them in different technology. For the background, this paper transformed the problem of unrecognizable color restoration into a semantic segmentation problem using U-Net MobileNet. For the golden edges, this paper used traditional image processing technology to obtain them from the color maps generated by the semantic segmentation algorithm. For the dragon patterns, after sketching the skeletons according to the dragon patterns, the image translation algorithm Pix2pix was applied to generate a realistic dragon pattern. Finally, the three repair results are superimposed to complete the repair. The virtually restored paintings can provide reference and guidance for traditional manual restoration and help the restorers to imagine what they looked like before oxidation, thus alleviating some repetitive work and reducing the complexity of restoration. Besides, each step of the method could form a layer, so that the restorers could overlay or modify them as they wish. [ABSTRACT FROM AUTHOR]

The paper presents a new algorithm for the image inpainting problem. The algorithm uses a recently designed versatile library of quasi-analytic complex-valued wavelet packets (qWPs) which originate from polynomial splines of arbitrary orders. Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions. For example, a set of the fourth-level qWPs comprises 62 different directions. The properties of these qWPs such as refined frequency resolution, directionality of waveforms with unlimited number of orientations, (anti-)symmetry of waveforms and windowed oscillating structure of waveforms with a variety of frequencies, make them efficient in image processing applications, in particular, in dealing with the inpainting problem addressed in the paper. The obtained results for this problem are quite competitive with the best state-of-the-art algorithms. The inpainting is implemented by an iterative scheme, which expands the Split Bregman Iteration (SBI) procedure by supplying it with an adaptive variable soft thresholding based on the Bivariate Shrinkage algorithm. In the inpainting experiments, performance comparison between the qWP-based methods and the state-of-the-art algorithms is presented. • The paper presents a new algorithm for image inpainting. • It uses a recently designed versatile library of quasi-analytic complex-valued wavelet packets (qWPs). • qWPs originate from polynomial splines of arbitrary orders. • Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions. • The properties of the qWPs include unlimited number of orientations of waveforms and their windowed oscillating structure with a variety of frequencies. • These properties make them efficient in image processing applications. • The obtained results for the inpainting problem are competitive with the best state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better. [ABSTRACT FROM AUTHOR]

Image processing methods for automated concrete crack detection are often challenged by binary noise. Noise removal methods decrease the false positive pixels of crack detection results, often at the cost of a reduction in true positives. This paper proposes a novel method for binary noise removal and segmentation of noisy concrete crack images. The method applies an area threshold before reducing the pixel groups in the image to a skeleton. Each skeleton is connected to its nearest neighbour before the remaining short skeletons in the image are removed using a length threshold. A morphological reconstruction follows to remove all elements in the original noisy image that do not intersect with the skeleton. Finally, pixel groups in close proximity to the endpoints of the pixel groups in the resulting image are reinstated. Testing was conducted on a dataset of noisy binary crack images; the proposed method (Skele-Marker) obtained recall, precision, intersection over union, and F1 score results of 77%, 91%, 72%, and 84%, respectively. Skele-Marker was compared to other methods found in literature and was found to outperform other methods in terms of precision, intersection over union and F1 score. The proposed method is used to make crack detection results more reliable, supporting the ever-growing demand for automated inspections of concrete structures. • We use image processing techniques to remove noise in binary concrete crack images. • False positives in binary concrete crack images can be reduced using our method. • We compare our concrete crack noise removal method to others found in literature. • Our method performed better than other concrete crack segmentation methods. [ABSTRACT FROM AUTHOR]

A new Infrared (IR) image analysis system will be deployed for real-time divertor protection during the upcoming Operational Phase (OP) 2.1 in the Wendelstein 7-X (W7-X) stellarator. Its primary objective is to prevent thermal overloads from permanently damaging Plasma-Facing Components (PFCs), resulting in machine downtimes and repair costs. The real-time constraint for this system is 110 ms, which is the maximum allowed delay entailing the acquisition, calibration, processing, and interlock, while all processing steps have to complete within 10 ms allowing for processing longer than the acquisition time of 10 ms at 100 Hz. This paper describes the implementation, real-time processing performance and detection effectiveness of Thermal Overload Detection (TOD). The implemented and evaluated TOD system fulfils real-time constraints. It reduces the total system delay to 50 ms and provides high detection sensitivity of 0.97 for archived discharge sequences from the OP1.2 campaign. The attained acceleration is significant, i.e., a 95% and 99% decrease in runtime for the sequential Central Processing Unit (CPU) and parallel Graphics Processing Unit (GPU) implementations, respectively, compared to the initial Python prototype. For the first time, the presented results confirm the feasibility of protecting W7-X in real-time comprising fundamentals for further advanced protection and control. • The first real-time protection of the plasma-facing components in Wendelstein 7-X. • The primary OP2.1 system to provide safe operation with the water-cooled components. • Accelerated full-frame processing of high-resolution infrared images with GPU and CPU. • Highly sensitive overload prediction algorithm for effective machine protection. • Evaluation and analysis of the algorithm on the archived discharges from OP1.2. [ABSTRACT FROM AUTHOR]

The alternating direction method of multipliers (ADMM) is an efficient splitting method for solving separable optimization with linear constraints. In this paper, an inertial proximal partially symmetric ADMM is proposed for solving linearly constrained multi-block nonconvex separable optimization, which can improve the computational efficiency by considering the ideas of inertial proximal point and linearization technique. Meanwhile, the proposed method updates the Lagrange multiplier twice and considers different relaxation factors in each iteration. Under the assumption that the generated sequence is bounded and the auxiliary function satisfies the Kurdyka-Łojasiewicz property, the global convergence of the proposed method with a more relaxed parameter range is analyzed. Moreover, some numerical results on SCAD (for smoothly clipped absolute deviation), image processing and robust PCA nonconvex problems are reported to demonstrate the efficiency and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

• An image denoising algorithm based on an image-dependent basis inspired by the quantum many-body theory. • The local structural similarity of images is formalized through a term akin to interaction in quantum physics. • The versatility of the adaptive basis extends its scope of applications regardless of the image characteristics and noise statistics. [Display omitted] Sparse representation of real-life images is a very effective approach in imaging applications, such as denoising. In recent years, with the growth of computing power, data-driven strategies exploiting the redundancy within patches extracted from one or several images to increase sparsity have become more prominent. This paper presents a novel image denoising algorithm exploiting such an image-dependent basis inspired by the quantum many-body theory. Based on patch analysis, the similarity measures in a local image neighborhood are formalized through a term akin to interaction in quantum mechanics that can efficiently preserve the local structures of real images. The versatile nature of this adaptive basis extends the scope of its application to image-independent or image-dependent noise scenarios without any adjustment. We carry out a rigorous comparison with contemporary methods to demonstrate the denoising capability of the proposed algorithm regardless of the image characteristics, noise statistics and intensity. We illustrate the properties of the hyperparameters and their respective effects on the denoising performance, together with automated rules of selecting their values close to the optimal one in experimental setups with ground truth not available. Finally, we show the ability of our approach to deal with practical images denoising problems such as medical ultrasound image despeckling applications. [ABSTRACT FROM AUTHOR]

Conventional noise reduction algorithms have been used in image processing for a very long time, but recently, deep learning-based algorithms have been shown to significantly reduce the noise in CT images. In this paper, a comparison of CT noise reduction of a deep learning-based, a conventional, and their combined denoising algorithms is presented. A conventional adaptive 3D bilateral filter and a 2D deep learning-based noise reduction algorithm and a combination of these are compared. For comparison, we used the noise power spectrum and the task transfer function which were measured on original CT images and the effective dose saving factors were also calculated. The noise reduction effect, the noise power spectrum and the task-transfer function are studied using Catphan 600 phantom and 26 clinical cases with more than 100,000 images. We also show that the effect of noise reduction of a 2D deep learning-based algorithm can be further enhanced by using conventional 3D spatial noise reduction algorithms. [ABSTRACT FROM AUTHOR]

• The proposed Anti-Gravity Stem-Seeking (AGSS) root image restoration algorithm can quickly and effectively repair root images. • AGSS root image restoration algorithm has good universality and does not need training data and high-performance hardware platforms. • Drought and waterlogging resistances of maizes can be effectively evaluated by the phenotypic parameters of repaired seed roots. Root phenotype detection is key to cultivating seeds with excellent traits, and requires a complete root image. However, soil occlusion, uneven lighting, and other factors cause broken points and segments in the root image. To solve this problem, an anti-gravity stem-seeking (AGSS) root image restoration algorithm is proposed in this paper to repair root images and extract root phenotype information for different resistant maize seeds. First, the obtained root image was processed using uniform illumination, grayscale, binarization, and morphological filtering to separate it from the background. Subsequently, a root skeleton map was generated using a thinning algorithm for pixel-level image processing, and the Taproot junction G was obtained. Subsequently, root pixel coordinates were obtained by traversing the root skeleton map. All root-segment endpoint coordinates were obtained using the endpoint judgment rule and stored in the endpoint list. The lateral and primary root endpoints were separated based on the lateral root judgment rule, and stored in the side root and primary root endpoint lists, respectively. Subsequently, the primary root endpoints were processed and fitted in the order short to long using arbitrary-two-endpoint spacing until all breakpoints of the primary root were found. The coordinates of the top endpoints of each principal root were obtained. Finally, the top endpoint was connected to point G based on the Bezier curve-fitting method to achieve complete root repair. The proposed AGSS root image restoration algorithm was applied to detect the root systems of maize with different resistances and wheat to evaluate its performance against the standard dataset. The results indicated a detection accuracy of greater than 90% for root taproot length and diameter. It was also found that maize drought resistance was positively correlated with root length and diameter, but negatively with the lateral root number. In contrast, the waterlogging and salt resistance traits of maize were positively correlated with the number of lateral roots. In conclusion, the proposed AGSS root image restoration algorithm can quickly and effectively repair root images, is suitable for different resistance evaluations of maize seeds, and is conducive to the detection of root phenotypes. Compared with deep learning methods, this algorithm displays advantages of fast repair, low hardware platform requirements, and less requirement of training images. The algorithm is highly suitable for deploying in small embedded systems, with broad application prospects. [ABSTRACT FROM AUTHOR]

• In this work, hidden topography under paint were characterized by dynamic speckle image processing. • The choice of an algorithm for dynamic speckled image processing was made with the lighting profile in mind. • The results show that the performance of various intensity normalized speckle algorithms decreases with the number of frames used. The use of qualitative algorithms in images with dynamic speckle allows generating a three-dimensional intensity map, which correlates with the sample activity. Samples in an evaporation state will present temporary mobility related to their volume, which allows characterizing their topography. However, the quality of the topographic characterization depends on the illumination profile and the number of images or frames used. In this paper, a review of various qualitative processing algorithms is carried out in order to evaluate their robustness to the number of frames and their dependence on the beam profile, evaluating the characterization of topography hidden by a layer of paint in process of drying. We use an aluminum structure with perforations of different diameters and depths as a sample. Among the algorithms used, we highlight the results obtained by the normalized DJC method, which characterizes the topography of our sample with a correlation of 0.98 and presents stability to the number of frames used. Thus, with these results we validate the use of dynamic speckle in the characterization of a surface covered with paint. [ABSTRACT FROM AUTHOR]

Image segmentation has been an important technique in the field of image processing. Fine-level manual annotations are very limited and difficult for a large collection of imbalanced images, where each image contains quite different small objects. However, we find that these imbalanced images have similar decision graphs obtained by a lightweight and simple clustering algorithm Density Peaks (DPeaks). Hence, in this paper, we propose a weakly supervised image segmentation algorithm. It trains a decision curve from decision graphs of a few sample imbalanced images by SVM and Support Vector Regression (SVR), which is effective for identifying the sparse region of an imbalanced image. Besides, RangeTree is applied to accelerate RDP for large images due to the high complexity of DPeaks Clustering. Experiments prove that the proposed algorithm works well on imbalanced image data sets, not only it can recognize main things, but also has the ability to identify some relatively small objects. [ABSTRACT FROM AUTHOR]

• Pixel level segmentation algorithm of irregular aggregate based on deep learning. • Online measurement system of stacking aggregate gradation containing water and mud. The quality of image processing affects the results of aggregate particle size detection. So far, the aggregate image segmentation is still limited to the traditional watershed algorithm (WA) and threshold segmentation algorithm, and their segmentation effect is limited. Therefore, a deep learning algorithm (DLA) to segment stacked coarse aggregate images was presented in this paper. Then it was used to segment the images of aggregates with different aggregate materials, particle sizes and mixing ratios, and the segmentation results were analyzed. The dataset required was made by the labelme, and trained to obtain the segmentation model of the DLA. The test results were compared with that of the traditional WA. For single-stage aggregates with particle sizes of 5–10 mm, 10–20 mm and 20–31.5 mm, the proportion of cumulative gradation calculated by DLA was increased by 5.93%, 28.62% and 23.62%, respectively, for different base materials of coarse aggregate, the proportion of cumulative gradation calculated by DLA was increased by 28.62%, 37.68% and 12.78%, respectively. For aggregate images with different mixing ratios, the segmentation results of DLA were closer to the actually configured gradation. The experimental results showed that the DLA could avoid the common phenomenon of over-segmentation and under-segmentation in the WA, and had general applicability in segmenting coarse aggregates with different particle sizes and aggregate materials. [ABSTRACT FROM AUTHOR]

Acoustic microscopy has found various applications from nondestructive testing (NDT) to medical imaging for quantitative information. Scanning acoustic microscope (SAM) used for imaging and sample preparation have significant bearing on the conclusions drawn. In most cases while scanning the sample, there appear lots of artifacts due to the presence of an inclination offset between the focal plane and the sample surface, which often leads to wrong conclusions. In this paper, we have demonstrated a novel algorithm which can correct this offset in post-processing, which contributes to more accurate quantitative information and qualitatively better images of the sample. The correction is performed for both the amplitudes as well as the time-of-flight. The proposed algorithm was tested in simulation as well as through experimentation for sensitivity and accuracy. The results are presented and they indicate significant correction of inclination with small error margins. The application of these algorithms can be found in real-time image processing a lot of industries as well as in research. • Correcting artifacts produced due to specimen's inclination. • Simulation has been performed for algorithm robustness. • Reliability of the algorithm verified on unknown biological sample. [ABSTRACT FROM AUTHOR]

Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize fuel behavior, like fuel swelling. However, due to the lack of technology or resources, many of the images have not been processed or were processed manually through visual examination. This paper represents first-attempt to develop an image processing algorithm capable of automatically extracting information regarding the degree of fuel swelling from neutron radiography imaging. The algorithm was applied to 120 images of three different metallic fuel pin compositions—U-10Zr, U-8Pu-10Zr, and U-19Pu-10Zr. The algorithm performs operations of image intensity adjustment, image binarization, region finding, and labeling to extract information about fuel swelling. The average growth for U-10Zr was found to be 8.49% with 95% Confidence Interval (CI) [8.33 –8.66%], for U-8Pu-10Zr — 7.50% with 95% CI [7.23 – 7.78%], and for U-19Pu-10Zr — 3.15%, with 95% CI [2.40 – 3.91%]. The results obtained by applying this automatic image processing algorithm are consistent with previously reported studies of the same types of fuels. The automatic image processing algorithm will be expanded to include thousands of available neutron radiography images and different types of fuels to investigate empirical dependencies of fuel swelling, which can be subsequently applied to advanced fuel modeling. Results from this study can later be compared to BISON simulations to further benchmark modeling efforts and develop assessment cases. [ABSTRACT FROM AUTHOR]

Image enhancement can accentuate image feature and is necessary process in image processing. This work focuses on fusing multi-exposure image sequences low-light image enhancement. Inspired by the classical non-local means in computer vision, we proposed an improved deep neural network framework with attentions for image enhancement. Firstly, the original image was preprocessed in different dimensions. we get the edge images using an edge extracted algorithm and fusion multi exposed images to get an better initial images based on fully convolutional neural network with position and channel attention mechanism. Secondly, the head network is constructed by fully convolutional neural network. For capturing long-range dependencies between features maps, we designed a non-local attention module for head network to get better enhancement image. Finally, emerging the original images, edge image and fusion image as the input of the head network, it can enhance the images to get high-quality images. Experiments show that our framework proposed in this paper is effective and the attention mechanism play a significant hole in the network. • Designed channel and space attention mechanism to fusion multi exposure images. • Designed non-local attention mechanism in feature maps domain for enhancement network. • Proposed an emerging deep neural network framework with multiple attention mechanism. [ABSTRACT FROM AUTHOR]

Scattering and viscous (or sticky) liquid-based media such as that in the human ear can be considered a highly turbid environment through which imaging a target is challenging. Typically, reduced target visibility and image quality together with the limitation of depth through which objects may be resolved occurs because of multiple scattering events. Consequently, several approaches described in the literature have been applied to image objects hidden within turbid liquid media. In this paper, we experimentally demonstrate an approach to enhance image quality during recovery of objects hidden in sticky liquid at different viscosity levels by the fusion of single-shot projections assisted by a non-local mean (NLM) filtering algorithm. The turbidity of the medium was evaluated via attenuation coefficient by Beer's law and the viscosity was measured by a capillary viscometer device. The medium was illuminated with a laser beam and multiple images of the object were obtained from different perspectives using a lens array. Following image processing, each sub-image (projection) was digitally cropped, extracted from the array, filtered, shifted to a common center and then summed with other sub-images to form a single average image. Different setup conditions such as: medium turbidity, target shape, illumination state, and filtering processing were tested and compered during this work. A quantitative image quality metric of visibility commonly used in image processing was adopted to evaluate the quality of the reconstructed targets. The feasibility of the proposed hybrid method is supported by the experimental results which show a reconstructed image with high visibility. This study is a part of our ongoing efforts to enable imaging and reveal objects embedded in turbid media by integrating different strategies from the disciplines of: optics, astrophysics, digital image processing, and medicine. [ABSTRACT FROM AUTHOR]

Superpixel is one of the most popular image over-segmentations with broad applications in the computer vision field to reduce their computations by replacing pixels as primitives. The main concerns of one superpixel generation algorithm are its accuracy and efficiency. One of the most important things in superpixel accuracy is to fit the image boundaries tightly with a few pixels as possible (namely minimal contour density, which is measured by the percent of superpixel contour pixels in the whole image). In this paper, we propose a new fast algorithm based on the clustering method to produce superpixels accurately with low contour density. First, we adopt the linear path from a pixel to one superpixel seed to define a regular term and propose a new distance measurement between them. In addition, we introduce the gradient and Local Binary Pattern (LBP) features and propose formulas of parameters in the proposed method adaptively. In this way, we can use the new distance measurement to group pixels as initial regions adaptively and produce the final superpixels by merging those small ones. Finally, we test the new algorithm on two public datasets and compare it with the state-of-the-art. Our method can generate superpixels with lower contour density while being competitive in accuracy and computational time. [ABSTRACT FROM AUTHOR]

The new form of artificial intelligence training, i.e. federated learning, is becoming more popular in the last few years. It is an optimization problem that includes additional mechanisms such as aggregation and data transmission. In this paper, we propose a hybridization of this type of training with a meta-heuristic. The meta-heuristic algorithm is adapted to manage the entire process as well as to analyze the best models to minimize attacks on this type of collaboration. The proposed solution is based on minimizing the general model error, with additional control mechanisms for incoming models, or adapting the aggregation method depending on the quality of the model. The innovative solution has been analyzed in terms of its application to the problem of image classification using classical and convolutional neural networks, and the most popular meta-heuristic algorithms. The proposal was analyzed in terms of the accuracy of the general model as well as for security against poisoning attacks. We reached 91% of accuracy using the proposed method with the Red Fox Optimization Algorithm and 95% in terms of detection of poisoned samples in the database. • A hybridization of meta-heuristic methods with federated learning. • A meta-heuristic for managing training process of neural networks. • A new technique for analyzing models and choosing the best ones for aggregation. • Additional verification of models to reduce the likelihood of an attack by poisoning. • A mechanism for cleaning the data set in case of its poisoning. [ABSTRACT FROM AUTHOR]

Existing research works in scene image classification have focused on different aspects such as content features (e.g., visual information), context features (e.g., annotations, semantic information, etc.) and both. However, such works suffer from various issues such as higher feature size and lower classification performance. In this paper, we propose a new feature extraction approach for scene image representation using two kinds of rich information: content features and context features. Specifically, the new content features are generated by multi-scale foreground and background information. Similarly, the new context features are generated by the novel compact supervised codebook. Our compact supervised codebook minimizes irrelevant and redundant information, which, in result, achieves the lower-sized contextual feature vector. Finally, we combine both content and context features to represent the scene image. Our experiments on three widely used benchmark scene datasets using Support Vector Machine (SVM) classifier reveal that our proposed context and content features produce better results than existing context and content features, respectively. The fusion of the proposed two types of features significantly outperform numerous state-of-the-art features. • Propose a novel contextual feature extraction method for scene images. • Propose a novel unique tokens extraction algorithm. • Aggregate multi-scale content and contextual features to represent the scene images. • Evaluate our proposed method on three commonly-used benchmark datasets. [ABSTRACT FROM AUTHOR]

• This article proposes a novel nonconvex TGV model for adaptive image restoration. • We develop in detail a highly efficient alternating direction method of multipliers. • The regularization parameter λ can be automatically estimated during the iteration. • Based on convex analysis, a novel convergence proof of our algorithm is presented. • Our scheme provide promising performance in restoration accuracy and visual quality. Overcoming the staircase effect and simultaneously preserving edge details is an important and challenging issue in image processing. To this aim, this paper investigates the nonconvex total generalized variation regularization model for image restoration. Numerically, a highly efficient alternating direction method of multipliers is constructed to deal with the optimization problem in detail, which closely incorporates the superiorities of iteratively reweighted ℓ 1 algorithm and variable splitting technique. The proposed strategy can efficiently achieve image restoration and adaptive parameter estimation by applying Morozov's discrepancy principle. Furthermore, the convergence property of our novel algorithm with the variable regularization parameter is presented. Finally, numerical simulations concertedly illustrate that our approach outperforms several state-of-the-art regularization models, in terms of reconstruction accuracy and edge-preserving ability. [ABSTRACT FROM AUTHOR]

This paper presents a centralized, vision-based method for robust, on-the-fly 3D localization and mapping of human crowds in large-scale outdoor environments, assuming their independent visual detection on the camera feed of multiple UAVs. The proposed method aims at enhancing vision-assisted human crowd avoidance, in line with common UAV safety regulations, since the resulting 3D crowd annotations may be employed by other algorithms for on-line mission/path replanning during deployment of a UAV fleet. Initially, 2D crowd heatmaps are assumed to be derived per video frame on-board each UAV separately, using deep neural human crowd detectors, which indicate the probability of each pixel depicting a human crowd. The UAV-mounted cameras are assumed to be covering the same large-scale outdoor area over time. The heatmaps of each time instance are transmitted to a central computer and back-projected onto the common 3D terrain/map of the navigation environment, utilizing the intrinsic and extrinsic camera parameters. The projected crowd heatmaps derived from the different drones/cameras are fused by exploiting a Bayesian filtering approach that favors newer crowd observations over older ones. Thus, during flight, an area is marked as crowded (therefore, a no-fly zone) if all, or most, individual UAV-mounted visual detectors have recently and confidently indicated crowd existence on it. In order to calculate prior probabilities for Bayesian fusion, the method also proposes and exploits a simple, but efficient image processing-based algorithm for identifying flat terrain areas (under the assumption that people do not gather on highly curved or inclined terrain), relying on a priori available ground elevation data for the mapped area. Evaluation on both synthetic and real-world multiview video sequences depicting human crowds in outdoor environments verifies the effectiveness of the proposed method. [Display omitted] • An engineering pipeline for on-line semantic 3D map annotation • A novel Bayesian on-line multiview fusion of map annotations • An novel algorithm for identifying large and flat terrain DEMs areas • A vision-based human crowd on-map localization by UAV fleet. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hierarchical neural network structure to extract various features of road distress. • Training and prediction method with multiple loss functions and weighted soft voting. • Computationally efficient road damage detection with high recognition performance. • Multiple road damage detection with an accuracy of 81.62% m-IoU and 79.33% F1. • Real-time performance algorithm for personal mobility vehicle safety. In this paper, we propose a novel neural network structure and training and prediction methods. We propose a novel deep neural network algorithm to detect road surface damage conditions for establishing a safe road environment. We secure 1300 training and 400 testing images to train the neural network; the images contain multiple types of road distress. The proposed algorithm is compared with nine deep learning models from various fields. Comparison results indicate that the proposed algorithm outperforms all others with a pixel accuracy of 97.61%, F1 score of 79.33%, mean intersection over union of 81.62%, and frequency-weighted intersection over union of 95.64%; in addition, it requires only 3.56 M parameters. In the future, the results of this study are expected to play an important role in ensuring safe driving by efficiently detecting poor road conditions. [ABSTRACT FROM AUTHOR]

• The network can directly learn end-to-end mapping between low-resolution images and high-resolution images, and it can use convolution and deconvolution cascade coding to eliminate noise. • Apply dilated convolution layer in the parts, so that small convolution kernel can obtain a large receptive field, which can improve the reconstruction effect and reduce the calculation amount. • Use residual learning method to quickly optimize the network. By adding multiple connections to the network, speed up the training speed and improve the performance. The existing image processing methods had aimed at the problems of blurred image reconstruction, large noise, and poor visual perception. The improved image super-resolution algorithm based on mixed deep convolutional networks is proposed in the paper. Firstly, the proposed method can shrink the low-resolution image to the specified size in upsampling phase. Secondly, it can extract features from low-resolution images. It sends the extracted initial features into the convolutional coding and decoding structure for image features. Thirdly, the feature extraction and calculation in high-dimensions are performed using dilated convolution in reconstruction layer. The high-resolution image had been reconstructed. The proposed method had been compared with state-of-arts on Set5, Set14, BSD100, and Urban100 datasets. The experimental results can show that the Peak Signal-to-Noise Ratio is increased by some ranges, and the Structural Similarity is increased by some effective percentage points. [Display omitted] [ABSTRACT FROM AUTHOR]

In the work described in this paper, we experimentally demonstrate an efficient method to improve reconstruction of objects embedded in turbid media. The method combines multiple polarized speckle projections and a fully automated sorting algorithm. In the experimental setup, the medium was illuminated with a polarized laser beam and multiple polarized images of the object were obtained from different viewing perspectives using a lens array and captured by a camera. In offline image processing, each sub-image was digitally cropped, extracted from the array, and qualitatively evaluated by contrast-to-noise ratio (CNR) and entropy metrics. Sub-images were sorted based on quality as determined by CNR and entropy values, with high CNR and low entropy considered as highest quality with the best resolution and contrast. The images were then shifted to a common center and then summed with other sub-images to form a single average image. Experiments to demonstrate the effectiveness of the proposed algorithm were carried out on targets with different geometrical shapes embedded within scattering media in transmission configuration. Analysis of image metrics indicated improvement in object reconstruction by using circular polarization as compared to the use of linear polarization or non-polarization states. The overall experimental results obtained in this work illustrate effective performance of the proposed method. [ABSTRACT FROM AUTHOR]

Accurately segmenting adjacent regions in images is one of the greatest challenges in image processing. In this paper, we introduce a double level set segmentation model based on mutual exclusion, which can accurately and independently segment adjacent regions. In order to avoid the creation of intersection regions and ensure the independence of neighboring regions, the model uses two level set functions, and defines the mutual exclusion term of neighboring regions according to the area of the joint region segmented by the two level set functions. Furthermore, we manually set the approximate contours of the target region as a prior information, defining prior constraints in the energy functional to ensure that the model only segments the organization we wanted. Combining the data term, the mutual exclusion term, the prior constraint term, the length term and the regularization term, we give the level set formulation of this double level sets energy functional and apply the gradient descent algorithm to solve its energy minimization problem. Applying the double level sets model to brain MR images, the experimental results show that the model can accurately and independently segment adjacent tissues in brain. The comparison experimental results with other models show that the double level sets model has a higher accuracy than the classical models in segmenting adjacent tissues. In addition, experiments on synthetic images show that the double level sets model can accurately segment images with intensity inhomogeneity and is robust to noise. Code is avaliable at https://github.com/ruicx/Mutual-Exclusion-Level-Set. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to establish a new mathematical framework for knowledge measure (KM) from fuzzy sets (FSs) through interval-valued intuitionistic fuzzy sets (IVIFSs). A novel KM is developed first in the context of intuitionistic fuzzy sets (IFSs) based on the normalized Hamming distance combined with the technique for order preference by similarity to ideal solution (TOPSIS), complying with the latest axiomatic definition. More efforts are made to investigate this new kind of axiomatic system and measuring model in the contexts of IVIFSs and FSs, respectively. This allows us to further recognize the nature of knowledge and present a unified framework for KM from FSs through IVIFSs. The developed technique is then used for image thresholding as a brand-new KM application, in which a new classification rule of pixels and a more efficient method for (interval-valued) intuitionistic fuzzification of images are proposed, thus leading to a knowledge-driven thresholding methodology. Experimental results show the outperformance of the developed technique with application. This is the first attempt to apply the latest KM theory to image processing, with which we undoubtedly create a new instance for the potential application areas of this theory. • New axiomatic definitions of IVIFKM and FKM are presented, respectively. • A unified framework for KM is developed from ordinary FSs through general IVIFSs. • The developed KMs are then used for image thresholding as a brand-new KM application. • New classification rule of pixels and fuzzification algorithm of images are proposed. • This is the first attempt to apply the latest KM theory to image processing. [ABSTRACT FROM AUTHOR]

• We propose a novel nano-scale absolute linear displacement measurement based on image recognition method. • We use a linear array image sensor to recognize the coding pattern on the grating and then realize the decoding. • We propose a high-resolution subdivision algorithm based on the location de-noising and fusion operation. • We set up an experimental system and test the performance of the proposed algorithm. There is urgent demand for new absolute Linear Displacement Measurement (LDM) technology with high resolution and high precision, during the development of high-end numerical control technology. In previous research, we found that displacement measurement based on the image processing method performs well, however, the volume increases when using an imaging lens, and the system is susceptible to factors such as virtual focus. In this paper, the lensless LDM image optical path is established based on grating projection imaging, then the absolute grating coding method is operated based on M −sequence pseudo-random coding. A linear displacement subdivision algorithm is then deployed based on the digital image recognition algorithm. An LDM device with a measuring range of 250 mm was designed to test the performance of the proposed method. The device shows a measurement resolution of 1 nm and measurement accuracy of 1.76 μ m in the range of 250 mm. This work lays a foundation for further research on high performance LDM technology. [ABSTRACT FROM AUTHOR]

Infrared pedestrian segmentation is a challenging problem in infrared image processing. Kaniadakis entropy thresholding method can segment the infrared image with long tail distribution histogram. However, it failed in images with noise and complex background. In this paper, a novel infrared pedestrian segmentation algorithm based on the two-dimensional Kaniadakis entropy thresholding is proposed to address this problem. First, in order to introduce the spatial information of pixels, the two-dimensional Kaniadakis entropy thresholding algorithm is proposed through extending the one-dimensional Kaniadakis entropy thresholding algorithm and using the two-dimensional histogram of image. The recursive formulas of the two-dimensional algorithm are also presented to improve the computation efficiency. Second, an intensity suppressed strategy is embedded in the optimal thresholds searching process of the two-dimensional Kaniadakis entropy thresholding algorithm to segment the pedestrian in infrared images with complex background. In the experiment, the proposed algorithm is compared with the state-of-the-art image segmentation methods on several infrared images with different backgrounds. The experimental results verify the effectiveness of the proposed method both qualitatively and quantitatively. • Extend the Kaniadakis entropy thresholding to 2d. • Derive the recursive formulas of the 2d Kaniadakis entropy thresholding. • Propose a suppressed strategy in 2d-Kan for infrared pedestrian segmentation. • Verify the effectiveness of the proposed infrared pedestrian segmentation algorithm. [ABSTRACT FROM AUTHOR]

In applications such as image processing and machine learning, imprecision can be tolerated because of the nature of the application itself or the limitation of human senses. By using the approximate computation in parts of imprecision-tolerant applications, where the output quality can be slightly degraded, significant power, delay, or area reductions can be achieved. In this paper, three approximate full adders with reasonable accuracy, low power, and low delay are proposed. The effects of die-to-die (D2D) process variation on the threshold voltage of approximate full adders have been evaluated, and a method has been proposed to reduce the effects of variability. For evaluating the accuracy and the variability, these approximate full adders have been used and analyzed in the ripple carry adder structure and image Sharpening algorithm. In terms of power-delay-product (PDP), accuracy, and area for uniformly distributed inputs, one of the presented approximate full adders exhibits the best performance, and another one shows the best peak-signal-to-noise ratio (PSNR) for real images. [ABSTRACT FROM AUTHOR]