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Yaozong Zhang
Researcher at Wuhan Institute of Technology
Publications - 23
Citations - 394
Yaozong Zhang is an academic researcher from Wuhan Institute of Technology. The author has contributed to research in topics: Computer science & Noise reduction. The author has an hindex of 6, co-authored 9 publications receiving 240 citations. Previous affiliations of Yaozong Zhang include Huazhong University of Science and Technology.
Papers
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Progressive Dual-Domain Filter for Enhancing and Denoising Optical Remote-Sensing Images
TL;DR: The quantitative and qualitative results of experiments demonstrate that the proposed DDF performs well on contrast enhancement, structure preservation, and noise reduction, and its satisfactory computation time resulting from its simple implementation makes it suitable for extensive application.
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Joint Analysis and Weighted Synthesis Sparsity Priors for Simultaneous Denoising and Destriping Optical Remote Sensing Images
TL;DR: This work proposes a unified variational framework, called a joint analysis and weighted synthesis (JAWS) sparsity model, to simultaneously separate the clean image and the stripe from a single optical remote sensing image.
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Spatially adaptive denoising for X-ray cardiovascular angiogram images
TL;DR: The comparison results of the extensive experiments on clinical X-ray cardiovascular angiogram images further illustrate that the proposed SAID method can yield clearer cardiovascular images which can provide more useful vascular information for clinicians to analyze and diagnose the cardiovascular diseases.
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Unidirectional variation and deep CNN denoiser priors for simultaneously destriping and denoising optical remote sensing images
Zhenghua Huang,Zhenghua Huang,Yaozong Zhang,Qian Li,Zhengtao Li,Tianxu Zhang,Nong Sang,Shiqi Xiong +7 more
TL;DR: Deep convolutional neural network denoiser prior is integrated into unidirectional variation model, named as UV-DCNN, to simultaneously destripe and denoise optical remote sensing images, which can be efficiently solved by the alternating minimization optimization method.
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Robust contact-point detection from pantograph-catenary infrared images by employing horizontal-vertical enhancement operator
TL;DR: An efficient contact-point detection (CPD) scheme to detect contact-points from these complex infrared images, including the following three key components, including an improved RANSAC strategy, is presented.