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Image denoising is an important pre-processing process in the fields of computer vision and image processing. Traditional denoising techniques blur edges excessively and degrade image quality by removing noise components but failing to maintain clarity. To overcome these problems, this paper proposes a multispectral image denoising strategy combining non-local rank tensor decomposition (NLRTD) and bilateral filtering. To extract patches from noisy images, single-level discrete wavelet transform (DWT) is utilized. Then, similar patches from the extracted images are grouped using spectral clustering. After that, mixed noise is reduced by separating clean images from each clustered group using NLRTD. An optimized bilateral filter using Sunflower optimization (SFO) is used for denoising by preserving edge details and is reconstructed using its constituent parts. The effectiveness of the proposed denoising method is assessed using performance matrices, such as BER, PSNR, MSE, RMSE, SNR and SSIM were 0.8544%, 53.21%, 2.41%, 2.41%, 25.06% and 0.90%, respectively. [ABSTRACT FROM AUTHOR]

Introduction. Formation of a contact surface in prosthetics and orthotics is crucial for the restoration of human musculoskeletal functions. This paper considers specific features of methods currently used for denoising of a 3D model surface obtained by optical scanning. An algorithm for manufacturing an individual prosthetic and orthopedic product is developed. The current literature reports no similar methods, which may be explained by the widespread use of gypsum technology for the manufacture of prostheses and orthoses.Aim. Research and development of digital filtration methods for 3D meshes obtained by optical scanning for further modeling of individual prosthesis and orthosis modules.Materials and methods. It is proposed to optimize the pre-processing stage of 3D scanned models by applying denoising and smoothing processes. In total, 50 optical 3D scans were selected for testing via the following denoising algorithms: bilateral filtering, vertex-based anisotropic smoothing, mean and median filtering applied to face normals.Results. The study was conducted using 3D scans of lower limb stumps and Chenault brace orthoses and corsets provided by the Institute of Prosthetics and Orthotics, Albrecht Federal Scientific and Educational Centre of Medical and Social Expertise and Rehabilitation. А method for denoising and smoothing of the 3D model surfaces for the manufacture of prosthetic and orthopedic products is proposed. The SNR metric difference SNR – δ-SNR (with averaging by scans and SNR values) and average execution time were calculated. The bilateral filtration method with δ-SNR = 11.3362 dB and a runtime of 8.8900 s showed the highest efficiency.Conclusion. The proposed methods for the pre-processing stage of 3D optical scans showed high efficiency in the formation of 3D models of prosthesis and orthosis modules. The results obtained can be used for automating the process of manufacturing various prosthetic and orthopedic products, which is particularly relevant in the context of the modern geopolitical situation.

Abstract Oral Squamous Cell Carcinoma (OSCC) causes a severe challenge in oncology due to the lack of diagnostic devices, leading to delays in detecting the disorder. The OSCC diagnosis through histopathology demands a pathologist expert because the cellular presentation is variable and highly complex. Existing diagnostic approaches for OSCC have specific efficiency and accuracy restrictions, highlighting the necessity for more reliable techniques. The increase of deep neural networks (DNN) model and their applications in medical imaging have been instrumental in disease diagnosis and detection. Automatic detection systems using deep learning (DL) approaches show tremendous promise in investigating medical imagery with speed, efficiency, and accuracy. In terms of OSCC, this system allows the diagnostic method to be streamlined, facilitating earlier diagnosis and enhancing survival rates. Automatic analysis of histopathological image (HI) can assist in accurately detecting and identifying tumorous tissue, reducing diagnostic turnaround times and increasing the efficacy of pathologists. This study presents a Squeeze-Excitation with Hybrid Deep Learning for Oral Squamous Cell Carcinoma Recognition (SEHDL-OSCCR) on HIs. The presented SEHDL-OSCCR technique mainly focuses on detecting oral cancer (OC) using hybrid DL models. The bilateral filtering (BF) technique is initially used to remove the noise. Next, the SEHDL-OSCCR technique employs the SE-CapsNet model to recognize the feature extractors. An improved crayfish optimization algorithm (ICOA) technique is utilized to improve the performance of the SE-CapsNet model. At last, the classification of the OSCC technique is performed by employing a convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) model. The simulation results obtained using the SEHDL-OSCCR technique are investigated using a benchmark medical image dataset. The experimental validation of the SEHDL-OSCCR technique illustrated a greater accuracy outcome of 98.75% compared to recent approaches.

Oral Squamous Cell Carcinoma (OSCC) causes a severe challenge in oncology due to the lack of diagnostic devices, leading to delays in detecting the disorder. The OSCC diagnosis through histopathology demands a pathologist expert because the cellular presentation is variable and highly complex. Existing diagnostic approaches for OSCC have specific efficiency and accuracy restrictions, highlighting the necessity for more reliable techniques. The increase of deep neural networks (DNN) model and their applications in medical imaging have been instrumental in disease diagnosis and detection. Automatic detection systems using deep learning (DL) approaches show tremendous promise in investigating medical imagery with speed, efficiency, and accuracy. In terms of OSCC, this system allows the diagnostic method to be streamlined, facilitating earlier diagnosis and enhancing survival rates. Automatic analysis of histopathological image (HI) can assist in accurately detecting and identifying tumorous tissue, reducing diagnostic turnaround times and increasing the efficacy of pathologists. This study presents a Squeeze-Excitation with Hybrid Deep Learning for Oral Squamous Cell Carcinoma Recognition (SEHDL-OSCCR) on HIs. The presented SEHDL-OSCCR technique mainly focuses on detecting oral cancer (OC) using hybrid DL models. The bilateral filtering (BF) technique is initially used to remove the noise. Next, the SEHDL-OSCCR technique employs the SE-CapsNet model to recognize the feature extractors. An improved crayfish optimization algorithm (ICOA) technique is utilized to improve the performance of the SE-CapsNet model. At last, the classification of the OSCC technique is performed by employing a convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) model. The simulation results obtained using the SEHDL-OSCCR technique are investigated using a benchmark medical image dataset. The experimental validation of the SEHDL-OSCCR technique illustrated a greater accuracy outcome of 98.75% compared to recent approaches. [ABSTRACT FROM AUTHOR]

Medical image segmentation (MIS) plays an important role in rapid disease detection and prognosis. U-Net, a widely used neural network in the field of MIS, encounters performance limitations when applied to complex datasets. These limitations are primarily due to the basic feature extraction blocks, namely the encoder and decoder, as well as the existence of a semantic gap between these two components. Some U-Net variants, such as Recurrent Residual U-Net, have attempted to solve the problem of simple feature extraction blocks by increasing the network depth. However, this approach does not effectively solve the semantic gap problem. In contrast, another variant, UNET + + , addresses the semantic gap problem by introducing dense skip connections but retains the simplicity of its feature extraction blocks. To overcome these challenges, a new approach is required. Therefore, this research proposed an optimized weight and loss function network called CNN-based Ebola U-Net (EU-Net) architecture for MIS. In the first step, the images are pre-processed for noise reduction using a filtering method called bilateral filtering, which is a hybridization of two Gaussian filters. The CNN-based EU-Net acts as both an encoder and a decoder with convolutional layers. A modification in the skip connection is encouraged to minimize the semantic gaps between the encoder and decoder. In each encoding stage, the edge information of the input images is extracted using the Pyramid Edge Extraction Module (PEEM). Using the extracted information, a segmentation map is generated on the decoder side. The Ebola Optimization Algorithm (EOA) is used to reduce the loss function and overall weight of the proposed network model. To evaluate the performance level of the proposed network, four different datasets are used. The proposed model achieves an overall accuracy of 97.33%, an RMSE of 1.36 and a Dice coefficient of 94.97%. [ABSTRACT FROM AUTHOR]

In light of the escalating advancements in architectural intelligence and information technology, the construction of smart cities increasingly necessitates a higher degree of precision in architectural measurements. Conventional approaches to architectural measurement, characterized by their low efficiency and protracted execution time, need to be revised to meet these burgeoning demands. To address this gap, we introduce a novel architectural image processing model that synergistically integrates Restricted Boltzmann Machines (RBMs) with Convolutional Neural Networks (CNNs) to facilitate the conversion of 2D architectural images into 3D. In the implementation phase of the model, an initial preprocessing of the architectural images is performed, followed by depth map conversion via bilateral filtering. Subsequently, minor voids in the images are rectified through a neighborhood interpolation algorithm. Finally, the preprocessed 2D images are input into the integrated model of RBMs and CNNs, realizing the 2D to 3D conversion. Experimental outcomes substantiate that this novel model attains a precision rate of 97%, and significantly outperforms comparative algorithms in terms of both runtime and efficiency. These results compellingly corroborate our model’s superiority in architectural image processing, enhancing measurement accuracy and drastically reducing execution time. [ABSTRACT FROM AUTHOR]

In the process of image acquisition, transmission, and storage, the image quality is often degraded due to a variety of unfavorable factors, resulting in information loss, which poses certain difficulties for subsequent image processing and analysis. How to enhance the visibility of image details and maintain the naturalness of the image is one of the important challenges in image processing. In response to this challenge, an image enhancement algorithm is proposed based on the advantages of histogram equalization and bilateral filtering. This algorithm organically integrates histogram equalization and bilateral filtering, aiming to improve image quality while reducing noise in the image. Specifically, the study first utilizes an improved histogram equalization strategy to preprocess the image and then applies a bilateral filter for further optimization. The experimental results showed that the optimized histogram equalization could effectively improve the global contrast of the image and avoid excessive enhancement and gray phenomenon of the image. Moreover, its peak signal-to-noise ratio could reach 0.71. However, bilateral filters showed significant advantages in processing complex data sets, and the peak signal-to-noise ratio could reach 0.95. It illustrated that the optimal research method has obvious advantages in improving image quality and reducing noise. The new enhancement strategy not only significantly improves the global contrast of the image but also preserves the naturalness of the image, providing important technical support for image analysis, machine vision, and artificial intelligence applications.

The aflatoxin produced by peanut mildew is highly carcinogenic, and it seriously affects food safety. In order to accurately and quickly identify moldy peanuts, this project proposes a detection method for moldy peanuts based on machine vision. Firstly, the peanut image was double-sided filtering and noise reduction, and then the image was converted to HSV space. The moldy peanut was recognized and detected by superimposing the mold color range extracted in H and S space and the open processing results of V space. The experimental results showed that the recognition accuracy of this method for moldy peanuts reached 95.3%, and the processing time for a single frame of peanut image was 0.6 seconds. Compared with other algorithms, this method had the advantages of fast speed and high accuracy, which can meet the real-time detection of moldy peanuts. At the same time, the grading processing of peanut mold is also more practical.

Malaria is a significant global health issue, responsible for the highest rates of morbidity and mortality globally. This paper introduces a very effective and precise convolutional neural network (CNN) method that employs advanced deep learning techniques to automate the detection of malaria in images of red blood cells (RBC). Furthermore, we present an emerging and efficient deep learning method for differentiating between cells infected with malaria and those that are not infected. To thoroughly evaluate the efficiency of our approach, we do a meticulous assessment that involves comparing different deep learning models, such as ResNet-50, MobileNet-v2, and Inception-v3, within the domain of malaria detection. Additionally, we conduct a thorough comparison of our proposed approach with current automated methods for malaria identification. An examination of the most current techniques reveals differences in performance metrics, such as accuracy, specificity, sensitivity, and F1 score, for diagnosing malaria. Moreover, compared to existing models for malaria detection, our method is the most successful, achieving an accurate score of 1.00 in all statistical matrices, confirming its promise as a highly efficient tool for automating malaria detection. [ABSTRACT FROM AUTHOR]

A novel method was proposed to enhance the quality of blurred infrared images captured during unmanned aerial vehicle (UAV) inspections of oil and gas pipelines. The issue of image deblurring was addressed by utilizing prior knowledge of image channels and employing bilateral filtering and the non-blind deconvolution network (NBDN) to remove artificial artifacts. Firstly, the dark channel prior knowledge was incorporated into a maximum a posteriori optimization framework by adding a dark channel L0 regularization term. Then, instead of using L0 regularization on image pixels, the L0 regularization term based on image gradients was employed as the constraint for the latent image. The blur kernel and the intermediate latent image were iteratively estimated through alternating estimation techniques and indirect optimization methods including semi-quadratic splitting and table lookup. The blur kernel was estimated using bilinear interpolation, and an image pyramid was constructed by upsampling and downsampling the image, which were then directly optimized by the conjugate gradient method. Finally, with the estimated blur kernel, a non-blind deblurring method based on the super-Laplacian prior was presented to obtain the latent imageI1, while another non-blind deblurring method based on L0 regularization was also applied to obtain the latent image I0. The difference map between I1 and I0 was calculated and then subtracted from I1 by bilateral filtering to obtain the final latent image I. The experiments were designed on low-light images, images with saturated pixels, real images, and infrared camera images to asses the proposed algorithm. The results show that the proposed method has strong competitiveness in various blurry image restoration effects. [ABSTRACT FROM AUTHOR]

Bilateral filtering is an image processing technique commonly adopted as intermediate step of several computer vision tasks. Opposite to the conventional image filtering, which is based on convolving the input pixels with a static kernel, the bilateral filtering computes its weights on the fly according to the current pixel values and some tuning parameters. Such additional elaborations involve nonlinear weighted averaging operations, which make difficult the deployment of bilateral filtering within existing vision technologies based on real-time and low-energy hardware architectures. This paper presents a new approximation strategy that aims to improve the energy efficiency of circuits implementing the bilateral filtering function, while preserving their real-time performances and elaboration accuracy. In contrast to the state-of-the-art, the proposed technique allows the filtering action to be on the fly adapted to both the current pixel values and to the tuning parameters, thus avoiding any architectural modification or tables update. When hardware implemented within the Xilinx Zynq XC7Z020 FPGA device, a 5 × 5 filter based on the proposed method processes 237.6 Mega pixels per second and consumes just 0.92 nJ per pixel, thus improving the energy efficiency by up to 2.8 times over the competitors. The impact of the proposed approximation on three different imaging applications has been also evaluated. Experiments demonstrate reasonable accuracy penalties over the accurate counterparts. [ABSTRACT FROM AUTHOR]

Indoor activity monitoring systems guarantee the security and well-being of aging and visually challenged persons living in their homes. These systems employ several sensors and technologies for monitoring daily activities such as sleep patterns, movement, and medication adherence, offering appreciated perceptions of the user’s day-to-day life and overall health. The adaptability and accuracy of the deep learning (DL) approach generate human activity recognition (HAR), an essential tool for improving security, effectiveness, and personalized experiences in indoor spaces. HAR deploying DL approaches revolutionizes indoor monitoring by allowing specific detection and understanding of human movements. Deep neural networks analyze data from several sensors such as accelerometers or cameras to distinguish intricate action patterns. DL approaches automatically extract and learn discriminative features, making them appropriate for recognizing complex human actions in sensor data. However, choosing the suitable DL structure and optimizing its parameters is vital for a better solution. This article introduces Indoor Activity Monitoring using the Chaotic Dwarf Mongoose Optimization with DL (IAM-CDMODL) technique for elderly and visually impaired people. The IAM-CDMODL technique mainly intends to detect indoor activities to ensure the safety of the elderly and visually impaired people. At the initial stage, the IAM-CDMODL technique follows a bilateral filtering approach for image preprocessing. In addition, the IAM-CDMODL technique exploits the MobileNetV2 (MNV2) model for learning complex and intrinsic patterns from the preprocessed images. Moreover, the CDMO model has been applied to the optimum choice of hyperparameters related to the MN-V2 approach. At the last stage, the deep convolutional neural network bidirectional long short-term memory method is applied to identify indoor activities. To ensure the improved detection performance of the IAM-CDMODL methodology, a wide range of simulations is executed on multiple cameras fall and UR Fall Detection datasets. The experimental validation of the IAM-CDMODL methodology portrayed a superior performance of 99.35% and 99.74% over recent models. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Cereals, Oils & Foods is the property of Science & Technology of Cereals, Oils & Foods Editorial Office 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.)

To improve the rendering effect of artistic images, a method enhancing features of artistic images is proposed based on histogram equalization and bilateral filtering in the article. Firstly, artistic images are divided into both high and low-frequency representations, and the multi-step enhancement processing level is delimited by multi-band decomposition. Secondly, the noise in the image is removed by bilateral filtering. Then, the grey-level histogram of the image is modified by using the histogram equalization. Finally, the features of the artistic image are enhanced by global tone mapping after histogram equalization processing is conducted. Then, the image is sharpened to improve the enhancement effect further. The experiments show that the features of the color and edge details turn out to be more vivid and clearer after the proposed method is implemented. The structural similarity (SSIM) measure of the image increases to 0.973, and the average gradient gets close to 0.8, which shows that the proposed method is effective. [ABSTRACT FROM AUTHOR]

The performance of the tire has a very important impact on the safe driving of the car, and in the actual use of the tire, due to complex road conditions or use conditions, it will inevitably cause immeasurable wear, scratches and other damage. In order to effectively detect the damage existing in the key parts of the tire, a tire surface damage detection method based on image processing was proposed. In this method, the image of tire side is captured by camera first. Then, the collected images are preprocessed by optimizing the multi-scale bilateral filtering algorithm to enhance the detailed information of the damaged area, and the optimization effect is obvious. Thirdly, the image segmentation based on clustering algorithm is carried out. Finally, the Harris corner detection method is used to capture the "salt and pepper" corner of the target region, and the segmsegmed binary image is screened and matched based on histogram correlation, and the target region is finally obtained. The experimental results show that the similarity detection is accurate, and the damage area can meet the requirements of accurate identification. [ABSTRACT FROM AUTHOR]

The human body is a typical non-rigid object, and its 3D reconstruction is a classic problem in the field of computer vision. Due to the inherent complexity and dynamism of the human body, it is not suitable for existing non-rigid 3D motion reconstruction algorithms that assume that the number of shape bases of non-rigid bodies is known. The number of shape bases is very important for 3D reconstruction methods. If the number of shape bases is estimated incorrectly in contour deformation estimation, it can lead to unreliable or even complete failure of the reconstruction algorithm. Therefore, this paper designs a 3D non-rigid motion human object reconstruction algorithm based on reliable estimation of contour deformation degree. This algorithm leverage Scale Invariant Feature Transform (SIFT) to obtain non-rigid moving human target features. Firstly, the contour appearance model of the moving human sequence is used to extract the contour feature sequence, which is preprocessed based on the contour appearance depth feature; Furthermore, the deformation degree of the contour is reconstructed and the calculation process of the number of shape bases was optimized, which is no longer simply defined as known. This method optimizes and solves the problem of missing data, improves the reliability of estimating the degree of contour deformation, and completes target reconstruction. The experimental results show that the three-dimensional reconstruction algorithm can accurately reconstruct the changes in the movements of athletes’ shots; The accuracy of 3D reconstruction can reach 95.98%; Moreover, PSNR, SSIM, and MSE indexes performed well with smaller fluctuation range, and the distribution of three-dimensional reconstructed scattering points is very close to the three-dimensional position distribution of real scattering points, and the three-dimensional reconstruction effect is good with strong reliability.

Dim and small target detection plays an important role in infrared target recognition systems. In this paper, we present a dim and small target detection algorithm based on improved bilateral filtering and Gaussian motion probability estimation, aiming to improve the detection efficiency of the detection system. First, a bilateral filtering algorithm based on image patch analysis is proposed to complete the background modeling, compare with single pixel, image patch contains more neighborhood information. Then, we use the Gaussian process combining the target position of consecutive $n$ frames to predict the target position of the $(n+1)\text{th}$ frame, and the target energy is accumulated along the trajectory direction at the same time. Finally, we construct the grayscale probability model to realize the multi-frame correlation detection, which combining the grayscale features and the motion characteristics of the target. Six scenes and eleven comparison algorithms are selected for experiments, experimental results show the effectiveness and robustness of the proposed algorithm.

Convolution is the inner product of the neighborhood signal and weights and plays a fundamental role in image processing; thus, acceleration of convolution is essential. Among convolutions, variable-weighted convolution is used in adaptive filters and edge-preserving smoothing to realize various applications. Some weights are replaced with lookup tables (LUTs) to accelerate these filters. LUT reference is a classical acceleration method. However, the difference between the growth rate in computing speed and memory I/O speed has limited the scope of utilization of LUT references. Speedup would be possible if registers could be used as LUTs, but their small size makes them difficult to utilize. Therefore, this study proposes a downsampling method to fit LUTs into SIMD registers, which are relatively large and an efficient reference method for register-LUTs. Experimental results show that the proposed method can reproduce an accuracy in PSNR of 65.52 (+25.11) dB, while a simple full-size LUT in the register size can only reproduce 40.41 dB. Using a wider register width, the PSNR was 78.63 (+38.22) dB with AVX-512 and 84.5 (+44.09) dB with bfloat16. The fastest proposed method was on average 4.82/3.72 times faster than direct vector computing, 2.99/3.10 times faster than vector addressing, and 3.79/7.80 times faster than scalar addressing on the AVX2/AVX-512 computers while exceeding the display limit of 60 dB for 8-bit displays. Taking into account these speed/accuracy trade-offs, the performance of the proposed method was superior. This paper shows that LUT references can be realized with small SIMD registers in convolution. The proposed method is expected to be extended to adaptive filters, convolutional neural networks, and other image processing applications by accelerating the approximation with this register-LUT. Our code is available at https://fukushimalab.github.io/registerLUT4conv/.

In order to improve the visualization effect of low resolution digital images, this study proposes a low resolution digital image reconstruction method based on Rational Function Model. Firstly, bilateral filtering is employed to preprocess low resolution digital images for denoising with preservation of considerable edge details of the images. Secondly, to make the digital images unlimited to the coordinate system in the reconstruction process, a Rational Function Model with general attributes is constructed. Data obtained from the model is taken as the input information, and the generative adversarial network is used to extract image features, which lays the data foundation for subsequent image reconstruction. Thirdly, SIFT algorithm and Difference of Gaussians function are used for accurate feature extraction to compensate for the extraction deviation caused by the defects of the training set itself in the generative adversarial network. Finally, the processes of feature point direction matching, wavelet transform, bilateral regularization processing, pixel correction, edge adaptive processing, etc. are carried out for ortho correction of the image function model. On this basis, image reconstruction is eventually established. The experiment shows the edges of the reconstructed digital image are non-aliased and relatively smooth, and the texture direction and shape in the original image are well maintained, so that the details of the target parts are greatly preserved, with high accuracy of feature point matching. Furthermore, the peak signal-to-noise ratio of the reconstructed digital image ranges from 90.3dB to 92.7dB, and the structural similarity index varies from 0.93 to 0.96, further demonstrating the effectiveness of this method. [ABSTRACT FROM AUTHOR]

In recent years, information technology has vastly improved. The quality of the image has been degraded by noise, which defeats the purpose of the noisy images. The major purpose of this paper is to find out which filters provide a better outcome while preprocessing medical images using computer tomography scans. The purpose of this paper is to remove noise from any images, whether they are real-time datasets or online datasets. To enhance an image for preprocessing, we have compared various filters; these filters are already available, but the major purpose is to identify the best filter. We compared the different parameters to find the best and finally found that the modified bilateral filtering provided a better result. The noise has been removed by using a bilateral filter, and the image clarity has not changed when using this filter. We have discussed the advantages and drawbacks of each approach. The effectiveness of these filters is compared using the peak signal-to-noise ratio, structural similarity index, contrast-tonoise ratio, and mean square error. The proposed algorithm is tested on 5 sample lung images. The results show that the modified bilateral filter produces better results. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Textile Technology is the property of Zhejiang Sci-Tech University Magazines 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.)

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui 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.)

Due to the difficulty in generating a 6-Degree-of-Freedom (6-DoF) object pose estimation dataset, and the existence of domain gaps between synthetic and real data, existing pose estimation methods face challenges in improving accuracy and generalization. This paper proposes a methodology that employs higher quality datasets and deep learning-based methods to reduce the problem of domain gaps between synthetic and real data and enhance the accuracy of pose estimation. The high-quality dataset is obtained from Blenderproc and it is innovatively processed using bilateral filtering to reduce the gap. A novel attention-based mask region-based convolutional neural network (R-CNN) is proposed to reduce the computation cost and improve the model detection accuracy. Meanwhile, an improved feature pyramidal network (iFPN) is achieved by adding a layer of bottom-up paths to extract the internalization of features of the underlying layer. Consequently, a novel convolutional block attention module–convolutional denoising autoencoder (CBAM–CDAE) network is proposed by presenting channel attention and spatial attention mechanisms to improve the ability of AE to extract images' features. Finally, an accurate 6-DoF object pose is obtained through pose refinement. The proposed approach is compared to other models using the T-LESS and LineMOD datasets. Comparison results demonstrate the proposed approach outperforms the other estimation models. [ABSTRACT FROM AUTHOR]

To identify counterfeit currency and report on the findings. Using a mobile camera, the model accepts the photograph. The extracted features from the scanning image are compared to a series of models. When a match is found, the outcome is outputted, indicating whether the match was true or not. Image resizing, image filtering, sobel edge detection, and template matching are the four algorithms used in this article. Even though printing false currencies is unlawful, counterfeit currencies continue to circulate in areas where there are no forms of verifying the currency's validity. The aim of this project is to avoid illicit notes from being distributed further. The project's aim is to identify false or counterfeit currency. It is accomplished by taking a sequence of steps in the same order each time. To begin, a cell phone is used to capture a picture of the currency note (camera). Second, the captured image is resized to or scaled down to 500 x 300 pixels. After that, a bilateral filter is used to eliminate noise from the signal. The features that determine a currency note's validity are then detected using the sobel operator. Correlation regression is used to match the characteristics of the note to those of an authentic note. Finally, features are listed and shown for the genuine note. [ABSTRACT FROM AUTHOR]

There are problems of under-enhancement and over-enhancement in commonly mine image enhancement methods such as histogram equalization, Retinex theory, homomorphic filtering, wavelet analysis, etc. In order to solve the above problems, a mine image enhancement method based on multi-scale local histogram equalization is proposed. According to the independent features of color components (hue component and saturation component) and brightness component of image in HSI color space, the low-light RGB mine image is converted into the HSI color space. The method uses bilateral filtering to decompose the brightness component into lighted images and reflected images. The method divides the lighting image into small, medium, and large blocks, and performs local histogram equalization on each image block to improve image brightness and contrast. The method performs 8-direction gradient enhancement on the reflected image to enrich the texture edges of the image. The method performs Retinex inverse transformation on the light image after multi-scale local histogram equalization and reflection image after directional gradient enhancement to obtain the enhanced brightness component. Then the brightness, hue and saturation components are transformed into RGB color space to obtain an enhanced mine image. Experimental verification of the mine image enhancement method based on multi-scale local histogram equalization is conducted by using actual monitoring images of coal mines. The enhancement effect is evaluated subjectively and objectively. The results show that compared with existing image enhancement methods, this method has a greater improvement in image brightness and contrast with richer detail information. The information entropy has increased by over 7.23%, and the mean average gradient has increased by over 31.6%. It has better image enhancement effects.

The traffic sign identification and recognition system (TSIRS) is an essential component for autonomous vehicles to succeed. The TSIRS helps to collect and provide helpful information for autonomous driving systems. The information may include limits on speed, directions for driving, signs to stop or lower the speed, and many more essential things for safe driving. Recently, incidents have been reported regarding autonomous vehicle crashes due to traffic sign identification and recognition system failures. The TSIRS fails to recognize the traffic signs in challenging conditions such as skewed signboards, scratches on traffic symbols, discontinuous or damaged traffic symbols, etc. These challenging conditions are presented for various reasons, such as accidents, storms, artificial damage, etc. Such traffic signs contain an ample amount of noise, because of which traffic sign identification and recognition become a challenging task for automated TSIRS systems. The proposed method in this paper addresses these challenges. The sign edge is a helpful feature for the recognition of traffic signs. A novel traffic sign edge detection algorithm is introduced based on bilateral filtering with adaptive thresholding and varying aperture size that effectively detects the edges from such noisy images. The proposed edge detection algorithm and transfer learning is used to train the Convolutional Neural Network (CNN) models and recognize the traffic signs. The performance of the proposed method is evaluated and compared with existing edge detection methods. The results show that the proposed algorithm achieves optimal Mean Square Error (MSE) and Root Mean Square Error (RMSE) error rates and has a better Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) ratio than the traditional edge detection algorithms. Furthermore, the precision rate, recall rate, and F1 scores are evaluated for the CNN models. With the German Traffic Sign Benchmark database (GTSRB), the proposed algorithm and Inception V3 CNN model gives promising results when it receives the edge-detected images for training and testing.

Chickpea is one of the most important legumes in the world, however, it is prone to various diseases that can significantly reduce its yield and quality. Hence, the accurate classification of these diseases are crucial for effective disease management. In this study, we propose a combined approach for chickpea disease classification using GLCM-Color Histogram features with Bilateral filtering and non-local means filtering. Our research comprises three phases: image preprocessing, feature extraction, and classification. To enhance the model's robustness and reduce noise, we applied Bilateral filtering, non-local means filtering, and data augmentation techniques. We utilized a combination of gray-level co-occurrence matrix (GLCM) and Color Histogram for feature extraction, which can capture the texture and color features important for image classification tasks. The extracted features were then classified using Multi-Layer Perceptrons (MLPs), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). The experimental results indicate that the combined features extracted using GLCM and Color Histogram with the SVM classifier outperformed individual feature extractors and classifiers, achieving a testing accuracy of 95.49 %. Our study demonstrates that proper image preprocessing, data augmentation, and feature extraction provide an efficient classification method for identifying and classifying chickpea disease.

In digital era, image can be easily forged by multiple manipulations using advance editing tools, such that truthfulness of that image cannot be identified by human eye. Many approaches have been proposed for the detection of these forged images. However, the performance of these approaches is quite better for large resolution and uncompressed images, whereas they fail for small-sized highly compressed images. To address this issue, a novel DCT-3DCNN architecture is proposed for multiple manipulation detection. The proposed DCT-3DCNN is constructed by stacking the DCTs of four residuals (Average filtering residuals, Gaussian filtering residuals, Laplacian filtering residuals and median filtering residuals) along depth-wise. The four DCTs are more capable to extract the manipulations traces in an image. These traces are fed into 3D-CNN to learn the low to high level features of multiple manipulations. Thus, the features are combined to classify the forged and pristine images. The performance of the proposed DCT-3DCNN is supported by exhaustive experiments for binary classification and multi- class classifications. Experiments are conducted on five (UCID, RAISE, BOSSBase, BOWS2 and NRCS) databases. The robustness of the proposed network is also evaluated for the detection of bilateral filtering on images. For binary classification, the improvement ratio (%) between the proposed (DCT-3DCNN) and state-of-the-art methods (MFR-CNN, RF-CNN) is 4–5%, while for bilateral filtering the improvement ratio (%) is 8% in comparison with the state-of-the art method RF-CNN. The proposed network achieves 14% improvement in detection accuracy for multi-class classification as compared to the RF-CNN. [ABSTRACT FROM AUTHOR]

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Denoising remote sensing images is crucial in the application and research of remote sensing imagery. Noise in remote sensing images originates from sensor characteristics, signal transmission, and environmental conditions, among which Gaussian noise is the most common type. In this paper, we proposed a multiple-optimization bilateral filtering (MOBF) algorithm based on edge detection and differential evolution (DE) methods. The proposed algorithm optimizes the spatial domain filtering kernel and the spatial domain Gaussian kernel by using the standard deviation and width of the edge response. By employing the DE algorithm, the individuals in the population based on the standard deviation of the gray value domain are subjected to iterative mutation, crossover, and selection operations to refine the latent solution vectors and determine the optimal color space for optimizing the standard deviation of the pixel range domain kernel. As a result, the MOBF algorithm, which does not require any parameter input, is realized. To verify the feasibility and effectiveness of the proposed algorithm, denoising experiments were conducted on remote sensing images by using evaluation metrics such as the mean squared error, peak signal-to-noise ratio, and structural similarity index. The experimental results revealed that the MOBF algorithm outperforms traditional algorithms for all three evaluation metrics. [ABSTRACT FROM AUTHOR]

Aiming at the strong spot noise caused by the reflection of meat slices in fish image with foreign matters that collected by ordinary camera or Single Lens Reflex (SLR) camera, we proposed an improved wavelet transform and bilateral filtering algorithm to reduce the noise of image. In this paper, our algorithm absorbed the advantages of wavelet threshold de‐noising algorithm and bilateral filtering algorithm, the wavelet threshold de‐noising algorithm being used for high frequency components and bilateral filtering being used for low frequency components, which not only achieved the effect of eliminating noise and weakening light spot, but also protected the details of the image. In the experiment, in order to verify the effectiveness of the proposed algorithm, we used the traditional bilateral filtering, Gaussian filtering and median filtering algorithms to process 500 salmon fish images which had residual fishbone, respectively. Since the original image itself was a noisy image, it was impossible to evaluate the noise reduction effect by using an algorithm that requires a noiseless image as a reference image, so we used five methods to evaluate the quality of the image processed by each algorithm, which were Blind/Referenceless Image Spatial QUality Evaluator (BRISQE), Cumulative Probability of Blur Detection (CPBD), variance, image entropy, and operation speed. Among them, BRISQE and CPBD could quantitatively reflect the overall quality of the image, the lower the BRISQE score or the higher the CPBD score, the better the quality of the image. Compared with other algorithms, the BRISQE score and CPBD score of the image processed by our algorithm were the best, with the average scores reaching 20.65 and.8421, respectively. Experimental results showed that the improved algorithm in this paper can effectively eliminate this kind of strong spot noise. The processed image has achieved better visual effect in both subjective and objective level. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 presents the hardware design of fast and low-cost denoising filters suitable to be exploited in the enabling technologies for Industry 5.0. A novel approximate computing strategy is introduced to reduce the computational complexity of the image denoising operation and to comply with real-time requirements. Firstly, it is demonstrated that the novel approximate approach can be helpfully exploited in the design of reconfigurable denoising filters able to reach image qualities as close as possible to the precise software counterparts. The reconfigurability leads to hardware architectures run-time adaptable to different levels of noise, whereas the adopted approximation strategy limits hardware resources and energy requirements. Quality tests, performed at various image and kernel sizes, and noise standard deviations, demonstrate that the approximate denoising approach presented here reaches PSNR and SSIM comparable with the precise denoise filtering. In comparison with state-of-the-art FPGA-based competitors, the novel filters reduce the resources requirements by up to 70%, achieve frame rates up to 35 times higher, and dissipate more than 45% lower power. When implemented within the XC7Z7020 FPGA device, a $5\times 5$ filter designed as proposed here denoises $512\times 512$ grayscale images using only 1689 LUTs, 2635 Flip-Flops and 32 DSPs. Moreover, it processes up to 926.8 frames per second, consumes just 63mW @ 244MHz and, with a noise standard deviation equal to 10, it achieves an average PSNR of $\sim 33$ dB with an average SSIM of ~0.86.

According to global survey reports, license plate recognition (LPR) provides rich information in approximating the traffic conditions of urban arterials is an emerging data source. Several researchers studied and investigated about segmenting, extracting and classifying the license plate and their approaches do not provide accurate extraction in the different weather condition (night, day, rainy, cloudy etc.). In this research work, a novel feature extraction technique called as Kernel Search Multiwavelet Decomposition (KsMWD) is proposed for license plate detection. By computing the dissimilarity search patterns, the binary and original value of the pixels are multiplied and converted with reference to the referenced pixel and its surrounding neighbours. The proposed segmentation produces an accuracy of 98.97% which is higher than any other existing algorithm. Depending upon the directions, the first-order derivatives are calculated for the projected information from the actual wave crested values. The efficiency of developed classification algorithm is found as 98.37% by effective combination with the horizontal edge density extraction. Finally, the proposed Inception Resnet V2 classification gives better accuracy than other segmentation method. Simulation results are included and performance analyses are tabulated for different weather conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department 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.)

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. 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.)

Using stereo-imaging systems to collect 3D information is innovative and flexible for underwater exploration. The stereo-matching of underwater image pairs is a significant and challenging task due to the poor visibility and the complex underwater light field. In this paper, we propose a novel underwater stereo-matching algorithm based on belief propagation(BP). We design the energy function suitable to apply in the underwater scenes. Specifically, we use zero-based normalized cross-correlation and Hamming distance to form the data term that computes a measure of similarity between points of the binocular image pair and design the smoothness term based on the color metric to settle the discontinuity of the disparity map. Furthermore, we use bilateral filtering to gather the initial matching cost and propose a filling operation for the occlusion in the disparity map. Extensive experiments demonstrate the effectiveness of the proposed algorithm both on simulated UW-Middlebury dataset and real-world underwater images pairs [ABSTRACT FROM AUTHOR]