imageprocessing (IP) technology has emerged on the basis of AI, digital imaging technology, and multimedia technology, and people began to use computers to process images to improve image quality and improve human vi...
imageprocessing (IP) technology has emerged on the basis of AI, digital imaging technology, and multimedia technology, and people began to use computers to process images to improve image quality and improve human vision. With the advancement of computer vision and AI technology, people want to achieve high performance IP. In the field of IP, techniques such as image segmentation, image compression, and image restoration are active research topics in computer vision. In this paper, we propose IP algorithms such as Artificial Bee Colony(ABC) Optimization Algorithm and Searcher Optimization Algorithm (SOA) for this block of image compression, analyze the image compression effect of these two algorithms, and compare the image compression quality by comparing the PSNR of each algorithm, and the results get that the image compression effect based on artificial bee colony(ABC) Algorithm is better and the image compression code book quality is better with the same compression ratio.
Tools for Transform Coding in coding of video relied on DCT-II traditionally for mapping residuals of image/video signals. Residual mapping can be done to a domain where quantizing and encoding tools give better effic...
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Hyperspectral imaging is a fast-growing imaging technique in many fields, like remote sensing, fruit analysis, clinical images, etc. The spectral image consists of two parts: spectral data and spatial data. The proces...
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We aim to enhance ophthalmologists’ decision-making when diagnosing the Neovascular Age-Related Macular Degeneration (nAMD). We developed three tools to analyze Optical Coherence Tomography Angiography images: (1) ex...
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ISBN:
(数字)9798331506520
ISBN:
(纸本)9798331506537
We aim to enhance ophthalmologists’ decision-making when diagnosing the Neovascular Age-Related Macular Degeneration (nAMD). We developed three tools to analyze Optical Coherence Tomography Angiography images: (1) extracting biomarkers such as mCNV area and vessel density using imageprocessing; (2) generating a 3D visualization of the neovascularization for a better view of the affected regions; and (3) applying an ensemble of three white box machine learning algorithms (decision tree, support vector machines and DL-Learner) for nAMD diagnosis. The learned expressions reached 100% accuracy for the training data and 68% accuracy in testing. The main advantage is that all the learned models white-box, which ensures explainability and transparency, allowing clinicians to better understand the decision-making process.
Dehazing plays a crucial role in enhancing image quality for successive imageprocessing applications. However, current single image dehazing algorithms demonstrate varied performance on different scenes. In this pape...
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ISBN:
(数字)9798350387780
ISBN:
(纸本)9798350387797
Dehazing plays a crucial role in enhancing image quality for successive imageprocessing applications. However, current single image dehazing algorithms demonstrate varied performance on different scenes. In this paper, we introduce an adaptive image dehazing scheme designed to enhance dehazing efficiency. Our approach involves classifying input images into 6 scenes through multiple feature extraction and analysis techniques. We have developed a method to select the top three appropriate dehazing methods from 15 ones, based on a comprehensive fusion evaluation of multiple indexes for each scene. Moreover, we have created specific color cast correction means to tackle two distinct issues. Comparative experiments on various collection images have verified the efficiency of our proposed method.
Hydraulic system as an important part of industrial automation, its teaching effect directly affects the quality of related technical personnel training. Hydraulic transmission course is a theoretical and practical co...
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ISBN:
(数字)9798331536169
ISBN:
(纸本)9798331536176
Hydraulic system as an important part of industrial automation, its teaching effect directly affects the quality of related technical personnel training. Hydraulic transmission course is a theoretical and practical combination of the post course offered by our non-commissioned officers' vocational and technical education in mechanical manufacturing technology, because the hydraulic system involves complex images and signal data, students often face difficulties in understanding and analyzing in the learning process. Through the course, students can systematically master the basic theoretical knowledge of the hydraulic transmission system, familiar with the structural composition and working principle of commonly used hydraulic components, and master the working principle and characteristics of common basic circuits. This paper proposes to combine image enhancement technology with signal processing methods to optimize the teaching of hydraulic system. By analyzing the application of image enhancement technology in image denoising, edge detection and brightness adjustment, as well as the practical effect of signal processing methods in frequency domain analysis, time-frequency analysis and intelligent algorithms, we explore how to enhance students' theoretical understanding and practical ability of hydraulic system. The study shows that the integration of image and signal processing technology can not only significantly improve the teaching efficiency, but also provide technical support for the construction of intelligent teaching mode in the future.
With the vigorous development of computer technology, its application in various fields is increasingly deepening, especially in the application of martial arts performance training models. This paper focuses on the p...
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The sparse reconstruction-based real-aperture imaging has significant application value in the field of radar forward-looking imaging. It is independent of target motion and capable of acquiring target image with few ...
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ISBN:
(数字)9798331541460
ISBN:
(纸本)9798331541477
The sparse reconstruction-based real-aperture imaging has significant application value in the field of radar forward-looking imaging. It is independent of target motion and capable of acquiring target image with few snapshots. However, the high computational complexity of super-resolution algorithms and the low geometric contour precision for spatially expanded targets limit the practical application of real-aperture imaging. This paper proposes a fast real-aperture imaging method of spatial-expanded targets based on weighted L1-norm, aiming to improve both imaging accuracy and efficiency. For the problem of blurred target contour, rough imaging results are used to assist fine angle measurement based on weighted Ll-norm, which could enhance the angle estimation precision. In order to tackle the challenge of high computational complexity, the array manifold dictionary is decomposed into a two-dimensional Kronecker product form. With the combination of the Woodbury identity and LADMM algorithm, the algorithm reduces matrix operation dimension, leading to the decreasing of the iteration numbers and the improvement of computational efficiency. Comparative experiments demonstrate that the proposed algorithm enhances both computational efficiency and spatial target imaging accuracy for real-aperture imaging.
image deblurring is an important and challenging problem in imaging processing. It aims to restore clear images from degenerated ones caused by camera shake or target motion. The total variation (TV) regularization ha...
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Hyperspectral image(HSI) band selection is a crucial task in imageprocessing. It is necessary to screen out bands with rich information and low correlation, to achieve data dimensionality reduction and retain key inf...
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ISBN:
(数字)9798350363999
ISBN:
(纸本)9798350364002
Hyperspectral image(HSI) band selection is a crucial task in imageprocessing. It is necessary to screen out bands with rich information and low correlation, to achieve data dimensionality reduction and retain key information, thereby improving imageprocessing efficiency. Aiming at the differences in reflectance characteristics of targets in different regions in hyperspectral images(HSls) and the strong correlation between bands, this paper proposes a gradient- guided spatial-spectral weighted hyperspectral band selection method. Firstly, gradient information is introduced to enhance the edge texture features of different targets. Then, the entropy rate superpixel segmentation algorithm is used to segment the first principal component of the image and divide multiple uniform regions. Secondly, to more comprehensively capture the global relationship between all bands in the image, a multi-graph complementary strategy is proposed to construct a regional similarity graph, aiming to maximize the mutual contribution between superpixels with spatial-spectral similarity. Finally, a unified clustering graph is generated by spectral clustering. After normalized cutting, the band with the least noise information in each sub-cube is selected to form a new band subset. The effectiveness of the proposed method is verified by classification results and data analysis.
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