Z
Zhenghua Huang
Researcher at Wuhan Institute of Technology
Publications - 58
Citations - 858
Zhenghua Huang 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 12, co-authored 35 publications receiving 502 citations. Previous affiliations of Zhenghua Huang include Huazhong University of Science and Technology.
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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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Iterative weighted sparse representation for X-ray cardiovascular angiogram image denoising over learned dictionary
TL;DR: The results of extensive experiments on clinical X-ray angiogram images illustrate that the proposed IWSR method performs well on noise reduction and vascular structures including edges and capillaries preservation, integral cardiovascular trees of which are beneficial for clinicians to diagnose and analyse cardiovascular diseases.
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Optical remote sensing image enhancement with weak structure preservation via spatially adaptive gamma correction
TL;DR: Results on real low-contrast optical remote sensing images demonstrate that the proposed image enhancement scheme outperforms the state-of-the-arts in terms of brightness improvement, contrast enhancement, and detail preservation.