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Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a weakly-supervised structural surface crack detection algorithm that can detect the crack area in an image with low data labeling cost. The algorithm consists of a convolutional neural networks Vgg16-Crack for classification, an improved and optimized class activation map (CAM) algorithm for accurately reflecting the position and distribution of cracks in the image, and a method that combines superpixel segmentation algorithm simple linear iterative clustering (SLIC) with CAM for more accurate semantic segmentation of cracks. In addition, this paper uses Bayesian optimization algorithm to obtain the optimal parameter combination that maximizes the performance of the model. The test results show that the algorithm only requires image-level labeling, which can effectively reduce the labor and material consumption brought by pixel-level labeling while ensuring accuracy. [ABSTRACT FROM AUTHOR]

Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images. [ABSTRACT FROM AUTHOR]

Generalized target detection algorithms perform well for large- and medium-sized targets but struggle with small ones. However, with the growing importance of aerial images in urban transportation and environmental monitoring, detecting small targets in such imagery has been a promising research hotspot. The challenge in small object detection lies in the limited pixel proportion and the complexity of feature extraction. Moreover, current mainstream detection algorithms tend to be overly complex, leading to structural redundancy for small objects. To cope with these challenges, this paper recommends the PCSG model based on yolov5, which optimizes both the detection head and backbone networks. (1) An enhanced detection header is introduced, featuring a new structure that enhances the feature pyramid network and the path aggregation network. This enhancement bolsters the model's shallow feature reuse capability and introduces a dedicated detection layer for smaller objects. Additionally, redundant structures in the network are pruned, and the lightweight and versatile upsampling operator CARAFE is used to optimize the upsampling algorithm. (2) The paper proposes the module named SPD-Conv to replace the strided convolution operation and pooling structures in yolov5, thereby enhancing the backbone's feature extraction capability. Furthermore, Ghost convolution is utilized to optimize the parameter count, ensuring that the backbone meets the real-time needs of aerial image detection. The experimental results from the RSOD dataset show that the PCSG model exhibits superior detection performance. The value of mAP increases from 97.1% to 97.8%, while the number of model parameters decreases by 22.3%, from 1,761,871 to 1,368,823. These findings unequivocally highlight the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

In order to effectively protect and verify the copyright information of multimedia digital works, this paper proposes a zero-watermarking algorithm based on carrier image feature point descriptors. The constructed feature matrix of this algorithm consists of two parts: the feature descriptor vector calculated from scale-invariant feature reconstruction transform (SIFRT) and the multi-radius local binary pattern (MrLBP) descriptor vector. The algorithm performs a standardization, feature decomposition, and redundancy reduction on the traditional keypoint descriptor matrix, combines it with the texture feature matrix, and achieves the dimensional matching of copyright information. The advantage of this algorithm lies in its non-modification of the original data. Compared to computing global features, the local features computed from a subset of key points reduce the amount of attack interference introduced during copyright verification, thereby reducing the number of erroneous pixel values that are introduced. The algorithm introduces a timestamp mechanism when uploading the generated zero-watermarking image to a third-party copyright center, preventing subsequent tampering. Experimental data analysis demonstrates that the algorithm exhibits good discriminability, security, and robustness. [ABSTRACT FROM AUTHOR]

Every year, the number of women affected by breast tumors is increasing worldwide. Hence, detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer. The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy. The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks. The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries. These drawbacks are resolved by the proposed cancer region detection methods stated in this paper. The mammogram images are classified into normal, benign, and malignant types using the Adaptive Neuro- Fuzzy Inference System (ANFIS) approach in this paper. This mammogram classification process consists of a noise filtering module, spatial-frequency transformation module, feature computation module, and classification module. The Gaussian Filtering Algorithm (GFA) is used as the pixel smooth filtering method and the Ridgelet transform is used as the spatial-frequency transformation module. The statistical Ridgelet feature metrics are computed from the transformed coefficients and these values are classified by theANFIS technique in this paper. Finally, Probability Histogram Segmentation Algorithm (PHSA) is proposed in this work to compute and segment the tumor pixels in the abnormal mammogram images. This proposed breast cancer detection approach is evaluated on the mammogram images in MIAS and DDSM datasets. From the extensive analysis of the proposed tumor detection methods stated in this work with other works, the proposed work significantly achieves a higher performance. The methodologies proposed in this paper can be used in breast cancer detection hospitals to assist the breast surgeon to detect and segment the cancer regions. [ABSTRACT FROM AUTHOR]

Fragile watermarking is embedding a watermark in a media (an image in this paper) such that even small changes, called tamper, can be detected or even recovered to prevent unauthorized alteration. A well-known category of spatial fragile watermarking methods is based on embedding the watermark in the least significant bits of the image to preserve the quality. In addition, Hamming code is a coding algorithm in communication that transmits the data bits by augmenting some check bits to detect and recover single-bit modifications precisely. This property was previously used to detect and perfectly recover the images modified by small tampers less than a quarter of the image in diameter. To achieve this goal, the Hamming code is applied on a distributed pixel, bits of which are gathered from sufficient far pixels in the image. It guarantees that such tampers can toggle at most one bit of each distributed Hamming code that is recoverable. It was the only guaranteed perfect reconstruction method of small tampers, based on our knowledge. In this paper, the method has been extended to support distortion in two bits of a Hamming code by the use of common structures of distributed codes. It guarantees the recovery of tampers less than half of the image in width and height. According to the experimental results, the proposed method achieved better performance, in terms of recovering the tampered areas, in comparison to state-of-the-art. [ABSTRACT FROM AUTHOR]

This paper proposes a new chaotic-based encryption algorithm to encrypt images securely and effectively. A combination of Arnold cat, logistic mapping and image blocking has been used to design this algorithm. The proposed algorithm is a special algorithm that not only does it use image blocking once to relocate the pixels in four areas of the image separately but also uses an Arnold cat mapping to relocate the pixels across the whole image once again. This results in a higher number of pixel relocations which makes it impossible to decode without the key. The other interesting and new point is the particular use of chaotic logistic mapping to develop three image keys and to combine them with the input image, and to develop four other image keys to encrypt the image. Relocation of the input image blocks and the images generated by the logistic mapping are conducted through sorting the numbers generated by the mapping and how they have been relocated. Then, Arnold cat is used to relocate the pixels across the whole image (which is a combination of the input image and three images generated by logistic mapping). Eventually, the encryption operation is conducted on the obtained image using the encryption key (the image generated through the combination of the four keys). Numerous statistical tests and security analyses indicate the excellent security of our proposed algorithm. [ABSTRACT FROM AUTHOR]

3D modeling is the most basic technology to realize VR (virtual reality). VOS (video object segmentation) is a pixel-level task, which aims to segment the moving objects in each frame of the video. Combining theory with practice, this paper studies the process of 3D virtual scene construction, and on this basis, researches the optimization methods of 3D modeling. In this paper, an unsupervised VOS algorithm is proposed, which initializes the target by combining the moving edge of the target image and the appearance edge of the target and assists the modeling of the VR 3D model, which has reference significance for the future construction of large-scale VR scenes. The results show that the segmentation accuracy of this algorithm can reach more than 94%, which is about 9% higher than that of the FASTSEG method. 3D modeling technology is the foundation of 3D virtual scene; so, it is of practical significance to study the application of 3D modeling technology. At the same time, it is of positive significance to use the unsupervised VOS algorithm to assist the VR 3D model modeling. [ABSTRACT FROM AUTHOR]

Aiming at the problems of identifying storage pest tobacco pest beetles from images that have few object pixels and considerable image noise, and therefore suffer from lack of information and identifiable features, this paper proposes an automatic monitoring method of tobacco beetle based on Multi-scale Global residual Feature Pyramid Network and Dual-path Deformable Attention (MGrFPN-DDrGAM). Firstly, a Multi-scale Global residual Feature Pyramid Network (MGrFPN) is constructed to obtain rich high-level semantic features and more complete information on low-level features to reduce missed detection; Then, a Dual-path Deformable receptive field Guided Attention Module (DDrGAM) is designed to establish long-range channel dependence, guide the effective fusion of features and improve the localization accuracy of tobacco beetles by fitting the spatial geometric deformation features of and capturing the spatial information of feature maps with different scales to enrich the feature information in the channel and spatial. Finally, to simulate a real scene, a multi-scene tobacco beetle dataset is created. The dataset includes 28,080 images and manually labeled tobacco beetle objects. The experimental results show that under the framework of the Faster R-CNN algorithm, the detection precision and recall rate of this method can reach 91.4% and 98.4% when the intersection ratio (IoU) is 0.5. Compared with Faster R-CNN and FPN, when the intersection ratio (IoU) is 0.7, the detection precision is improved by 32.9% and 6.9%, respectively. The proposed method is superior to the current mainstream methods. [ABSTRACT FROM AUTHOR]

With the development of near-eye display technology, optical distortion is often introduced into the design of near-eye optical system to meet people's requirements for high definition and large field of view. However, the larger the field of view, the greater the distortion of the projected image. In order to correct distorted images, this paper proposes an image pre-distortion algorithm based on nonlinear fitting and forward mapping interpolation. The radial basis function fitting algorithm is used to get a more precise mapping relationship between the original image and the pre-distorted image. The compactly supported radial basis function (CSRBF) interpolation algorithm based on forward mapping is used for image gray reconstruction to obtain better correction effect of image distortion. The experimental results show that the fitting error of the radial basis function fitting algorithm can be reduced to less than 1 pixel, reaching sub-pixel level. The pre-distortion algorithm has a good correction effect on the non-rotational symmetry distortion produced by the near-eye optical system, which makes the display image clear and complete. [ABSTRACT FROM AUTHOR]

Aiming at the current lane line detection algorithm in complex traffic scenes, such as lane lines being blocked by shadows, blurred roads, and road sparseness, which lead to low lane line detection accuracy and poor real-time detection speed, this paper proposes a lane line detection algorithm based on instance segmentation. Firstly, the improved lightweight network RepVgg-A0 is used to encode road images, which expands the receptive field of the network; secondly, a multi-size asymmetric shuffling convolution model is proposed for the characteristics of sparse and slender lane lines, which enhances the ability to extract lane line features; an adaptive upsampling model is further proposed as a decoder, which upsamples the feature map to the original resolution for pixel-level classification and detection, and adds the lane line prediction branch to output the confidence of the lane line; and finally, the instance segmentation-based lane line detection algorithm is successfully deployed on the embedded platform Jetson Nano, and half-precision acceleration is performed using NVDIA's TensorRT framework. The experimental results show that the Acc value of the lane line detection algorithm based on instance segmentation is 96.7%, and the FPS is 77.5 fps/s. The detection speed deployed on the embedded platform Jetson Nano reaches 27 fps/s. [ABSTRACT FROM AUTHOR]

This paper proposes a new one-dimensional chaotic map—nonlinear coupled Sine-Tent-Logistic chaotic map (1DNCSTL). A series of tests on this map show that the map has the characteristics of randomness and sensitivity to initial values and is suitable for image encryption. Based on this map, the article further proposes pixel bit position scrambling and reorganization operation and dynamic nonunique diffusion operation. Pixel bit position scrambling and reorganization operation is different from the traditional scrambling operation that only changes the position of the pixel value; this operation can achieve the effect of changing the pixel position and pixel value at the same time. The dynamic nonunique diffusion operation is different from the traditional unique-formula diffusion operation. The diffusion formula is not unique, which can ensure the security of the algorithm. Simulation experiment results and various security performance analysis show that the algorithm proposed in this paper has good performance. Compared with other encryption schemes, this algorithm is more suitable for image encryption. [ABSTRACT FROM AUTHOR]

Image inpainting is a challenging problem with a wide range of applications such as object removal and old photo restoration. The methods based on low-rank sparse prior have been used for regular or nearly regular texture inpainting. However, since such inpainting results do not synthesize the original pixels, they are usually not sharp especially when the area to be recovered is large. One remedy is to use an exemplar-based method. However, it often produces false matches and cannot obtain globally consistent inpainting results. In this paper, we give a new model to promote low rankness and sparsity and solve this model with a recently proposed Riemannian optimization algorithm. Furthermore, we propose a novel two-stage algorithm by integrating the low-rank sparse model with an exemplar-based method. Numerical experiments demonstrate that the proposed low-rank sparsity-based method and the two-stage algorithm achieve encouraging results compared to state-of-the-art image completion algorithms. [ABSTRACT FROM AUTHOR]

The process of sending confidential data through the communication media and in complete secrecy is now necessary, whether the data is related to patients, a particular military operation, or a specified office. On the other hand, with the development of various ciphering algorithms, and information hiding algorithms, there is a need to obtain ciphered and hidden data securely without the need to exchange secret keys between the two ends of the communication. In this paper, a hiding algorithm based on fused images and Caesar cipher with intelligent methods to strengthen the security of confidential information is proposed. Firstly, fused image scattering is obtained using 1’s complement and circularly shifting the bits of fused pixels by specified positions before the hiding process. Secondly, the keys for the Caesar cipher are derived from the length of secret information according to the mathematical equation. Thirdly, strengthen the security of Caesar’s cipher by taking a 1’s complement of each letter in the cipher data. The results guarantee the security of the presented algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a communication algorithm in which a grayscale image, shared between two parties, can be used to transmit a secret message, by applying the idea introduced in the B92 quantum protocol. The least significant qubits of the pixels' representation in certain regions of the image are used. With the help of a server, the algorithm generates a random message, which can further act as a secret key for cryptographic algorithms in order to secure the data that two parties might want to exchange later on. The chosen representation of the image is NEQR (novel enhanced quantum representation) and the platform used for testing the described idea is IBM Quantum Experience, along with the open-source Python framework called Qiskit. This solution allows users to design, implement quantum circuits (containing various quantum gates), and simulate them using real devices and local simulators. An implementation using this platform for a sample image and the corresponding results are also presented in this paper. [ABSTRACT FROM AUTHOR]

Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, edge regions, noise interference, etc. The epipolar plane image (EPI) of LF can effectively deal with the depth estimation because of its characteristics of multidirectionality and pixel consistency—in which the LF depth estimations are converted to calculate the EPI slope. This paper proposed an EPI LF depth estimation algorithm based on a directional relationship model and attention mechanism. Unlike the subaperture LF depth estimation method, the proposed method takes EPIs as input images. Specifically, a directional relationship model was used to extract direction features of the horizontal and vertical EPIs, respectively. Then, a multiviewpoint attention mechanism combining channel attention and spatial attention is used to give more weight to the EPI slope information. Subsequently, multiple residual modules are used to eliminate the redundant features that interfere with the EPI slope information—in which a small stride convolution operation is used to avoid losing key EPI slope information. The experimental results revealed that the proposed algorithm outperformed the compared algorithms in terms of accuracy. [ABSTRACT FROM AUTHOR]

Robust face clustering enjoys a wide range of applications for gate passes, surveillance systems and security analysis in embedded sensors. Nevertheless, existing algorithms have limitations in finding accurate clusters when data contain noise (e.g., occluded face clustering and recognition). It is known that in subspace clustering, the ℓ 1 - and ℓ 2 -norm regularizers can improve subspace preservation and connectivity, respectively, and the elastic net regularizer (i.e., the mixture of the ℓ 1 - and ℓ 2 -norms) provides a balance between the two properties. However, existing deterministic methods have high per iteration computational complexities, making them inapplicable to large-scale problems. To address this issue, this paper proposes the first accelerated stochastic variance reduction gradient (RASVRG) algorithm for robust subspace clustering. We also introduce a new momentum acceleration technique for the RASVRG algorithm. As a result of the involvement of this momentum, the RASVRG algorithm achieves both the best oracle complexity and the fastest convergence rate, and it reaches higher efficiency in practice for both strongly convex and not strongly convex models. Various experimental results show that the RASVRG algorithm outperformed existing state-of-the-art methods with elastic net and ℓ 1 -norm regularizers in terms of accuracy in most cases. As demonstrated on real-world face datasets with different manually added levels of pixel corruption and occlusion situations, the RASVRG algorithm achieved much better performance in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Scene text detection has been one of the most important topics for research in computer vision. With constant development and rise in deep learning, computer vision technology has undergone an impactful transformation. In the era before deep learning, there existed algorithms and technologies for scene text detection, but the performance was mediocre. In recent years, deep learning technology has remarkably transformed scene text detection. Researchers have witnessed notable advancements in the approach, methodology, and overall performance of the newly discovered techniques. In this paper, the predominant focus is on summarizing and analysing the significant progress in scene text detection through deep learning. This paper covers an introduction to scene text detection, steps to perform scene text recognition and detection, technique before deep-learning, recent techniques and their insights, some results, and an overview by comparing the algorithms. We will also emphasize the criteria that make a search algorithm a good choice for performing scene text detection and recognition, the notable differences incorporated by deep learning, and analyse the drawbacks of the techniques used before deep learning. This paper would be helpful to understand the key differences that have changed this field and also some remaining challenges. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Convolutional neural networks have achieved good results in target detection in many application scenarios, but convolutional neural networks still face great challenges when facing scenarios with small target sizes and complex background environments. To solve the problem of low accuracy of infrared weak target detection in complex scenes, and considering the real-time requirements of the detection task, we choose the YOLOv5s target detection algorithm for improvement. We add the Bottleneck Transformer structure and CoordConv to the network to optimize the model parameters and improve the performance of the detection network. Meanwhile, a two-dimensional Gaussian distribution is used to describe the importance of pixel points in the target frame, and the normalized Guassian Wasserstein distance (NWD) is used to measure the similarity between the prediction frame and the true frame to characterize the loss function of weak targets, which will help highlight the targets with flat positional deviation transformation and improve the detection accuracy. Finally, through experimental verification, compared with other mainstream detection algorithms, the improved algorithm in this paper significantly improves the target detection accuracy, with the mAP reaching 96.7 percent, which is 2.2 percentage points higher compared with Yolov5s. [ABSTRACT FROM AUTHOR]

This paper proposes the You Only Look Once (YOLO) dependency fusing attention network (DFAN) detection algorithm, improved based on the lightweight network YOLOv4-tiny. It combines the advantages of fast speed of traditional lightweight networks and high precision of traditional heavyweight networks, so it is very suitable for the real-time detection of high-altitude safety belts in embedded equipment. In response to the difficulty of extracting the features of an object with a low effective pixel ratio—which is an object with a low ratio of actual area to detection anchor area in the YOLOv4-tiny network—we make three major improvements to the baseline network: The first improvement is introducing the atrous spatial pyramid pooling network after CSPDarkNet-tiny extracts features. The second is to propose the DFAN, while the third is to introduce the path aggregation network (PANet) to replace the feature pyramid network (FPN) of the original network and fuse it with the DFAN. According to the experimental results in the high-altitude safety belt dataset, YOLO-DFAN improves the accuracy by 5.13% compared with the original network, and its detection speed meets the real-time demand. The algorithm also exhibits a good improvement on the Pascal voc07+12 dataset. [ABSTRACT FROM AUTHOR]

In today's world of technological advancement higher data rate and data transmission with minimal memory requirement is gaining importance. At the same time it also important to preserve quality of data to be transferred from various types of distortions and to assess the quality at the receiving end. Hence perceptual image quality assessment is becoming more popular. This paper introduces a new approach in domain of no reference image quality assessment of jpeg compressed images using spatial features. Considering the fact that pixel distortion, blurring and edge information are important aspects as far as distortions are concern. A novel approach is presented in this paper to address the distortion categories. The results are obtained in conjunction with the subjective image quality assessment of train and test image categories. Also results are compared with full reference image quality assessment technique and parameters of full reference quality assessment. To achieve better accuracy algorithm is tested on LIVE Texas' image database and our own developed database. Results are found to be well correlating to the various parameters considered for the comparison purpose. [ABSTRACT FROM AUTHOR]

This paper proposes a high linearity digital scanning algorithm (HLD), which quantifies the additional luminous current generated during the switching process of the driving transistor, and generates linear weights by adjusting the time and amplitude of the subfield, eliminating the low linearity of gray level caused by the row data zeroing operation of the fractional subfield in the traditional digital pulse width modulation (PWM) algorithm. The results indicate that by modulating Vcom, RMSE of grayscale data is increased from 3.8689 to 2.8826. In addition, this paper proposes a leakage reuse technique that collects the leakage current of the SRAM pixel array in the hold state to precharge the row line, reducing the loss of supply voltage by charging the row capacitors from 0 to 1, and effectively reducing the dynamic power consumption of the row by 22%. [ABSTRACT FROM AUTHOR]

Positioning technology is one of the key steps of automatic docking. At present, vehicle docking technology is widely used in airport baggage cars, towing trailers and other fields, and the docking process is mostly done manually. In order to realize the automatic docking of vehicles in mixed scenes, this paper proposes a fast positioning method for docking vehicles in mixed traffic scenes. First, using the regression relationship between the template size in the camera's field of view and the actual distance, FFT template matching and threshold judgment methods are employed to locate, track, and match the template, as well as verify the threshold results, in order to achieve template long-distance positioning and ROI extraction. Then, based on the regression relationship between AprilTag pixels in the camera's field of view and actual distance, the experiment of AprilTag minimum recognition pixel is designed. Finally, the real-time pixels of AprilTag are calculated from the real-time ranging results, and compared with the minimum recognition pixel of AprilTag, it is judged whether the recognition conditions are met, so as to carry out the subsequent AprilTag positioning. The experimental results demonstrate that the method proposed in this paper can achieve rapid and accurate positioning of a target docking vehicle at a long distance, while reducing the mismatch rate. On this basis, by designing the identification conditions of AprilTag, the rapid identification and positioning of AprilTag is realized, which reduces the traversal time of the algorithm. The feasibility of this positioning method is verified by the attitude angle error experiment, which provides supporting conditions for the subsequent docking path planning and vehicle control. [ABSTRACT FROM AUTHOR]

Steganography is widely recognized as an effective method for protecting information via digital media. This paper presents an innovative image steganography algorithm incorporating image compression, chaotic maps, and the least significant bit. The process begins with the compression of a confidential medical image using Huffman encoding. The compressed image then undergoes shuffling, facilitated by the chaotic logistic map. The bits from the shuffled image are discreetly embedded into randomly selected pixels of the cover image, guided by the chaotic piecewise smooth map. The resulting stego image is generated. Statistical analyses are applied to both the cover and stego images for evaluation. The proposed algorithm is compared against state-of-the-art algorithms, and the results demonstrate its superiority over existing methods. [ABSTRACT FROM AUTHOR]

This paper presents an innovative algorithm for unwrapping 2D phase maps with discontinuities. The method employs residue detection for identifying affected areas at a coarse scale. Unlike traditional techniques relying on subjective assessment, this algorithm automates threshold determination, ensuring precision without manual intervention. At the pixel level, it utilizes a bitmap mask based on second differences and the geometric mean formula to locate inconsistencies within the wrapped map precisely. This coarse-to-fine process establishes an optimal threshold for the second difference mask, resulting in highly accurate unwrapped outcomes while maintaining computational efficiency. Compared to conventional methods, this approach delivers superior unwrapped results, making it suitable for diverse applications. Experimental validation includes computer-simulated surfaces and practical fringe projection systems, accompanied by a thorough error analysis. [ABSTRACT FROM AUTHOR]

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies. [ABSTRACT FROM AUTHOR]

In binocular stereo matching, there has been a problem of low matching accuracy and noise immunity in discontinuous regions and weak‐textured regions. This paper proposes a stereo matching algorithm based on an improved adaptive support window. In the cost computation stage, first, according to the preset arm length and colour threshold, a cross‐based arm is obtained, which centres on the pixel to be matched; then the adaptive regions of the vertical arm and the horizontal arm are constructed respectively, which have different shape and size. Finally, the union of the two adaptive regions is used as the final support window of Census transform. Performance evaluations on Middlebury stereo data sets demonstrate that the proposed algorithm outperforms other seven most challenging stereo matching algorithms. The mismatching rate of this algorithm is greatly reduced, and the anti‐noise performance is also improved considerably. Because the construction of the adaptive region is based on strict criteria and comprehensive consideration, the algorithm proposed in the paper can improve the matching accuracy in the weak‐textured regions and discontinuous disparity regions. [ABSTRACT FROM AUTHOR]

The security analysis of the existing chaotic image encryption algorithm is mainly for the algorithm whose secret keys are independent of plaintext, while a few cryptanalyses on plaintext-related scheme are released. This paper analyzes the security of a chaotic encryption algorithm related to the sum of plaintext pixel value (SPPV). It is found that for all plaintexts with the same SPPV, the chaotic sequences used for encryption are identical. Accordingly, there exist a series of equivalent keys corresponding to the different SPPVs, a codebook attack method to crack this type of encryption algorithm is proposed. Since the SPPV is predictable, limited in number and reconfigurable, the chosen-plaintext attack is adopted to analyze the plaintext with all possible SPPVs. And the corresponding equivalent keys are calculated, respectively. These equivalent keys corresponding to SPPV finally form a complete codebook for cracking the original encryption scheme. The research result shows that any encryption algorithm related to SPPV can be cracked by the codebook attack method proposed in this paper. Therefore, the method proposed in this paper owns certain universal applicability. Theoretical analysis and experimental results prove the feasibility of this method. [ABSTRACT FROM AUTHOR]

The inverse halftoning technology refers to restoring a continuous-toned image from a halftoned image with only bi-level pixels. However, recovering the continuous images from their halftoned ones is normally ill-posed, which makes the inverse halftoning algorithm very challenging. In this paper, we propose an optimization model with two alternate projections (TAP) for image inverse halftoning under the weighted nuclear norm minimization (WNNM) framework. The main contributions are twofold. First, the WNNM nonlocal regularization term is established, which offers a powerful mechanism of nonlocal self-similarity to ensure a more reliable estimation. Second, the alternate minimization projections are formulated for solving the image inverse halftoning, which reconstructs the continuous-toned image without destroying the image details and structures. The experimental results showed that the proposed method outperformed the state of the arts in terms of both objective measurements and subjective visual performance. The codes and constructed models are available at: https://github.com/juneryoung2022/IH-WNNM. [ABSTRACT FROM AUTHOR]

In this paper, a new method for image mosaicing (image stitching) is introduced based on Scale Invariant Feature transform (SIFT). One of the main drawbacks of SIFT is the redundancy of the extracted keypoints, which leads to lower image mosaicing quality. Recently, a new method called Redundant Keypoint Elimination (RKEM) was presented to remove these redundant features, and enhance image registration performance. Despite the applicability of RKEM, its threshold value is considered the same in all parts of the image. This characteristic leads to inappropriate removal of keypoints due to the fact that distribution of keypoints in the high-detailed region is denser than the low-detailed ones. This paper proposes a new method to improve RKEM called Clustered RKEM (CRKEM) which is based on keypoints distribution. Moreover, in this paper a new blending algorithm is proposed based on a Gaussian-weighted function. In the proposed blending method, the Gaussian function is proposed based on the mean and variance of the pixels in the overlapped region of images to be mosiaced. In comparison with the classical methods, the experimental results confirm the superiority of the proposed method in image mosaicing as well as to image registration and matching. [ABSTRACT FROM AUTHOR]

This paper presents an optimal weighted modulus (OWM) algorithm able to conceal secret messages in a high dynamic range image encoded via the RGBE format, consisting of the red, green, blue, and exponent channels. In contrast to current state-of-the-art schemes, which mainly employ limited and vulnerable homogeneous representations, our OWM scheme exploits four channels and an embedding weight to conceal secret messages, thereby offering more embedding capacities and undetectability against steganalytic tools. To reduce the impact on the luminance variation, we confine the maximal change incurred in the exponent channel when embedding secret messages. In addition, we propose an SEC scheme to eliminate the pixel saturation problem, even though a pixel contains values close to the boundary extreme. As a result, the stego images produced not only exhibit high quality but also comply with the RGBE encoding format, making them able to resist malicious steganalytic detection. The experimental results show that our scheme offers larger embedding rates, between 2.8074 and 5.7549 bits per pixel, and the average PSNR value for twelve tone-mapped images is over 48 dB. In addition, the HDR VDP 3.0 metric demonstrates the high fidelity of stego HDR images, where the average Q value is close to the upper bound of 10.0. Our scheme can defeat RS steganalytic attacks and resist image compatibility attacks. A comparison result confirms that our scheme outperforms six current state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

In this paper, by combining Polyak's inertial extrapolation technique for minimization problem with the viscosity approximation for fixed point problem, we develop a new type of numerical solution method for split feasibility problem. Under suitable assumptions, we establish the global convergence of the designed method. The given experimental results applied on the sparse reconstruction problem show that the proposed algorithm is not only robust to different levels of sparsity and amplitude of signals and the noise pixels but also insensitive to the diverse values of scalar weight. Further, the proposed algorithm achieves better restoration performance compared with some other algorithms for image recovery. [ABSTRACT FROM AUTHOR]

This paper proposes PatternAttack to mitigate two major issues of Adversarial Transformation Network (ATN) including the low diversity and the low quality of adversarial examples. In order to deal with the first issue, this research proposes a stacked convolutional autoencoder based on patterns to generalize ATN. This proposed autoencoder could support different patterns such as all-pixel pattern, object boundary pattern, and class model map pattern. In order to deal with the second issue, this paper presents an algorithm to improve the quality of adversarial examples in terms of L 0 -norm and L 2 -norm. This algorithm employs adversarial pixel ranking heuristics such as JSMA and COI to prioritize adversarial pixels. To demonstrate the advantages of the proposed method, comprehensive experiments have been conducted on the MNIST dataset and the CIFAR-10 dataset. For the first issue, the proposed autoencoder generates diverse adversarial examples. For the second issue, the proposed algorithm significantly improves the quality of adversarial examples. In terms of L 0 -norm, the proposed algorithm decreases from hundreds of adversarial pixels to one adversarial pixel. In terms of L 2 -norm, the proposed algorithm reduces the average distance considerably. These results show that the proposed method can generate high-quality and diverse adversarial examples in practice. [ABSTRACT FROM AUTHOR]

The Landsat and Sentinel series satellites contain their own quality tagging data products, marking the source image pixel by pixel with several specific semantic categories. These data products generally contain categories such as cloud, cloud shadow, land, water body, and snow. Due to the lack of mid-wave and thermal infrared bands, the accuracy of traditional cloud detection algorithm is unstable when facing Chinese Gaofen-1/6 (GF-1/6) data. Moreover, it is challenging to distinguish clouds from snow. In order to produce GF-1/6 satellite pixel-by-pixel quality tagging data products, this paper builds a training sample set of more than 100,000 image pairs, primarily using Sentinel-2 satellite data. Then, we adopt the Swin Transformer model with a self-attention mechanism for GF-1/6 satellite image quality tagging. Experiments show that the model's overall accuracy reaches the level of Fmask v4.6 with more than 10,000 training samples, and the model can distinguish between cloud and snow correctly. Our GF-1/6 quality tagging algorithm can meet the requirements of the "Analysis Ready Data (ARD) Technology Research for Domestic Satellite" project. [ABSTRACT FROM AUTHOR]

Recently, a chaotic secure video communication system based on FPGA is proposed, which essentially encrypts the original image data of each frame in the video in multiple rounds by using the 6D self-synchronizing chaotic stream cipher. In this paper, the security performance of the multiround encryption algorithm is analyzed based on divide-and-conquer attack. In the case of one round encryption, the keys ψ i j (1 ≤ i ≤ 3 , 1 ≤ j ≤ 6) can be deciphered by setting 30 appropriate initial conditions according to known-plaintext attack. After that, according to chosen-ciphertext attack, the chaotic iterative equation of the decryption end is degenerated into a linear one if ciphertexts are all set to zero. Under this condition, the equations between the unknown keys and the plaintext-ciphertext pairs can be obtained by setting 24 appropriate initial conditions, and the keys ψ i j (4 ≤ i ≤ 6 , 4 ≤ j ≤ 6) can be deciphered by solving the equations in Mathematica. Secondly, in order to reduce the multiplication times of the unknown keys in the case of two-round encryption, the number of pixels in an image can be selected as little as possible. According to chosen-ciphertext attack, when the number of the pixels in an image is only one, the keys ψ i j (1 ≤ i ≤ 3 , 1 ≤ j ≤ 6) can be deciphered by setting 30 appropriate initial conditions. Then, when the number of pixels in an image is two, the keys ψ i j (4 ≤ i ≤ 6 , 4 ≤ j ≤ 6) can be deciphered by setting 24 appropriate initial conditions. However, in the case that the encryption round is three or more, the keys cannot be deciphered by the attack method proposed in this paper. The analysis results show that the multiround image encryption algorithm is not secure when the number of encryption rounds is one or two. [ABSTRACT FROM AUTHOR]

In recent years, human pose estimation has been widely used in human-computer interaction, augmented reality, video surveillance, and many other fields, but the task of pose estimation still faces many challenges. To address the large number of parameters and complicated calculation in the current mainstream human pose estimation network, this paper proposes a lightweight pose estimation network (Lightweight Polarized Network, referred to as LPNet) based on a polarized self-attention mechanism. First, ghost convolution is used to reduce the number of parameters of the feature extraction network; second, by introducing the polarized self-attention module, the pixel-level regression task can be better solved, the lack of extracted features due to the decrease in the number of parameters can be reduced, and the accuracy of the regression of human keypoints can be improved; finally, a new coordinate decoding method is designed to reduce the error in the heatmap decoding process and improve the accuracy of keypoint regression. The method proposed in this paper was evaluated on the human keypoint detection datasets COCO and MPII, and compared with the current mainstream methods. The experimental results show that the proposed method greatly reduces the number of parameters of the model while ensuring a small loss in accuracy. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present a new 2-stage hybrid optimization algorithm based scheme named PSO-BHA for image enhancement. A parameterized mapping function and a novel objective function are utilized in this paper to achieve the best-enhanced images. The suggested scheme combines the merits of particle swarm optimization (PSO) with the black hole algorithm (BHA) in two sequential stages to find the best parameters for the mapping function with the aid of the proposed objective function. The objective function uses contrast, edge, entropy, and universal quality index (UQI) for measuring contrast, and different improved information in the enhanced image. In the proposed scheme, PSO is applied first to adjust the tunable parameters of the mapping function and as a result, new pixel intensities are produced. Then, in the second stage, the obtained pixel intensities are again passed through the mapping function whose parameters are tuned by the use of the BHA. The suggested framework overcomes the limitations of the traditional histogram equalization (HE) based enhancement techniques in which excessive contrast enhancement and image information loss can occur. The suggested method is evaluated on several test images and compared with different state-of-the-art methods. The results indicate that the proposed framework provides superior performance to all existing methods in terms of various metrics. The proposed scheme also contributes to substantial feature enhancement and contrast boosting in the enhanced image, while retaining the natural feel of the original image. [ABSTRACT FROM AUTHOR]

VR(Virtual Reality) Display always need high PPI(Pixel Per Inch) and high resolution because of its near eye amplification effect and large viewing angle. It is not uncommon to find a 4K or 8K solution today. With the growth of PPI and resolution, display manufacture technology and data transmission become a much more harder problem. Central foveal technology is usually used in gazing point VR system, there is a lot of good ideas in camera and GPU technology, but it seems rarely have something that display panel can do. In this paper, we present a smart foveal VR display algorithm base on BOE SPR (Sub‐pixel Rendering) pixel arrangement(which is called BV3: BrightView3 by BOE) and smart view driving function. The change of pixel arrangement relieve the pressure of manufacture requirement, and SPR algorithm used to maintain the picture quality. And the change of panel's driving method help to solve the data transmission problem, we use a smart foveal algorithm to deal with the data of central foveal area and non‐foveal area separately. As a result, the algorithm can help to save 3/4 data transmission band width. [ABSTRACT FROM AUTHOR]

Ultrasonic through-transmission data processed using the back-projection algorithm offers depth and lateral information about a defect beyond the capabilities of current through-transmission techniques. This technique was trialled on a carbon steel block containing side-drilled holes. Imaging artefacts can arise from the use of the backprojection algorithm, due to applying a weighting of one to each pixel, irrespective of how much of the pixel is intersected by the beam. Noise can also occur within the image where there are few intersections of the pixels made. This is seen at the edges of the image. In this paper, a novel back-projection technique utilises the weighting of pixels, dependent on the normalised weight of the beam that intersects them, to reduce any artefacts that occurred previously due to the backprojection algorithm. This paper also explores the use of the algebraic reconstruction technique (ART) algorithm for noise removal, thus increasing the sharpness of the defect. [ABSTRACT FROM AUTHOR]

In this paper, a new mean matting method based on mean correlation is proposed to extract thin blood vessels precisely. The proposed algorithm is the combination of both supervised and unsupervised method. The supervised methodology performs well in extracting thick blood vessels; however, thin vessels are not precisely extracted. Even the capability of unsupervised method is better in extracting thin vessels; it has some artifacts in the output. The proposed method combines the advantages of both supervised and unsupervised method to extract vessel regions more precisely irrespective of their thickness. Using supervised methodology, thick blood vessels are extracted by training the classifier with the significant features describing the vessel regions. Trimap is generated on the unsupervised output and mean correlation is computed for all unknown pixels in the trimap to classify those pixels into vessels or background. The proposed matting method has less computational complexity compared to other existing matting methods. The performance of the proposed method is evaluated in detail on DRIVE and STARE datasets. [ABSTRACT FROM AUTHOR]

For improving LC light‐thru, the RGBW technology is proposed to implement peak brightness for high dynamic range display application. In current design, utilizing sub‐pixel rendering (SPR) places RGBW sub‐pixels on stripe array of 3 channels uniformly and keeps the same driving source line in three subpixels per pixel. The SPR algorithm is required to compensate it to pretend as similar display performance as real stripe RGB panel. The paper will discuss how W sub‐pixel is utilized to tune panel brightness and avoid possible issues for display colorful pictures. The paper would present the SPR, adaptive content backlight control (SPW+BL) and its HDR application (SPW+HDR) to dominate dynamic brightness range of RGBW panel. [ABSTRACT FROM AUTHOR]

Aiming at the problem that feature extraction is inaccurate and the reconstruction image is not natural enough in blind super-resolution reconstruction, this paper proposed an image blind super-resolution reconstruction based on degradation aware and sequence residuals. This paper proposed a mini-residual group combined degeneration aware and sequence residuals as the backbone network. Then the method constructed a symmetrical enhanced multi-scale residual block. In the image reconstruction part, this paper used the bottleneck attention module and the sub-pixel convolutional module to emphasize the multi-dimensional elements of the image. Finally, the method made a global residual connection. Compared with the current representative algorithm DASR, experiments show that the PSNR and SSIM of the proposed algorithm are improved 0.145dB and 0.0014 on Set14×2, and the PSNR of the proposed algorithm is improved 1.898dB and 0.252dB on Set14×3/4, respectively. The proposed algorithm achieves better performance than several current image super-resolution algorithms on five standard test sets. [ABSTRACT FROM AUTHOR]

Current bitmap collaboration models suffer from issues such as large volume of data transmission, poor flexibility, overlapping conflicts and storage pressure. To solve these problems, this paper proposes a collaborative editing model for complex bitmaps. Collaborative operation that sends only the coordinates and color change, featuring low data volume, multi-dimensional granularity, and overlap able. Adds a four-dimensional bitmap collaborative architecture with spatiotemporal relationship, and only saves the edited pixel data, which can save more storage space; A replica state control algorithm based on rollback that ensures operations are executed on correct replica. An algorithm that can dynamically generate branch versions according to the current state and policy configuration, solving the problem of too many branch versions. Finally, inferring model correctness based on the CCI model, develop a prototype collaborative editor “Co-Pixel”, execute experiments based on randomly generated operation sets, simulation of actual editing behavior against Adobe Lightroom. Results show that this model can avoid conflicts between independent operations to a greater extent, can reduce the algorithmic overhead in the consistency maintenance process, can flexibly adjust the intent retention policy, and can generate a version of the result that reduces the user’s intent maintenance effort. The model in this paper provides a good solution for collaborative bitmap editing. [ABSTRACT FROM AUTHOR]

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Background subtraction (BS) is one of the most commonly encountered tasks in video analysis and tracking systems. It distinguishes the foreground (moving objects) from the video sequences captured by static imaging sensors. Background subtraction in remote scene infrared (IR) video is important and common to lots of fields. This paper provides a Remote Scene IR Dataset captured by our designed medium-wave infrared (MWIR) sensor. Each video sequence in this dataset is identified with specific BS challenges and the pixel-wise ground truth of foreground (FG) for each frame is also provided. A series of experiments were conducted to evaluate BS algorithms on this proposed dataset. The overall performance of BS algorithms and the processor/memory requirements were compared. Proper evaluation metrics or criteria were employed to evaluate the capability of each BS algorithm to handle different kinds of BS challenges represented in this dataset. The results and conclusions in this paper provide valid references to develop new BS algorithm for remote scene IR video sequence, and some of them are not only limited to remote scene or IR video sequence but also generic for background subtraction. The Remote Scene IR dataset and the foreground masks detected by each evaluated BS algorithm are available online: https://github.com/JerryYaoGl/BSEvaluationRemoteSceneIR. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an improved multi-focus image fusion (MFIF) algorithm. Existing spatial domain algorithms dependent on the obtained fusion decision map still lead to unexpected ghosting, blurred, edges as well as blocking effects such that the visual effect of image fusion is seriously degraded. To overcome these shortages, an improved MFIF algorithm is developed with the help of a novel multi-scale weighted focus measure and a decision map optimization technique. First, a novel multi-scale measurement template is designed in order to effectively extract the gradient information of rich texture regions, smooth regions as well as transitional regions between the aforementioned regions simultaneously. Then, an improved calculation scheme of the focus score matrix is designed based on the weighted sum of the focus measure maps in each region window centered on a concerned pixel, under which the advantage of pixel-by-pixel weighting is employed. In what follows, an initial decision map is obtained in light of the focus score matrix combined with threshold filtering, which is employed to eliminate the small isolated regions caused by some misclassified pixels. Furthermore, an accurate decision map is received with the help of the optimization capability of guided filtering to avoid edge unexpected artificial textures. In comparison with block-based fusion algorithms, our algorithm developed in this paper extracts the focus regions pixel-by-pixel, thereby helping to reduce the blocking effects that appear in the fusion image. Finally, some intensive comparison analysis based on common datasets is performed to verify the superiority over state-of-the-art methods in both visual qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]

Within-image co-salient object detection (wCoSOD) identifies the common and salient objects within an image, which can benefit for many applications, such as reducing information redundancy, animation synthesis, and so on. Besides, the introduction of depth information that conforms to the stereo perception of human is also more conducive to accurately detecting salient objects. Thus, in this paper, we focus on a new task from the perspective of the benchmark dataset and baseline model, i.e., within-image co-salient object detection in RGB-D images. To bridge the gap the new task and algorithm verification, we first collect a new dataset containing 240 RGB-D images and the corresponding pixel-wise ground truth. Then, we propose an unsupervised method for within-image co-salient object detection in RGB-D images. Under the constraint of depth information, our model decomposes the within-image co-salient object detection task into two parts: determining the salient object proposals; combining the similarity constraint and cluster-based constraint between different proposals to locate the co-salient object and generate the final result. The experimental results on the collected dataset demonstrate that our method achieves competitive performance both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]