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Guangming Shi
Researcher at Xidian University
Publications - 488
Citations - 14046
Guangming Shi is an academic researcher from Xidian University. The author has contributed to research in topics: Computer science & Sparse approximation. The author has an hindex of 41, co-authored 428 publications receiving 10591 citations. Previous affiliations of Guangming Shi include Chinese Ministry of Education.
Papers
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Nonlocally Centralized Sparse Representation for Image Restoration
TL;DR: The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, and the extensive experiments validate the generality and state-of-the-art performance of the proposed NCSR algorithm.
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Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
TL;DR: Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.
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Two-stage image denoising by principal component analysis with local pixel grouping
TL;DR: Experimental results on benchmark test images demonstrate that the LPG-PCA method achieves very competitive denoising performance, especially in image fine structure preservation, compared with state-of-the-art Denoising algorithms.
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Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach
TL;DR: This paper takes a low-rank approach toward SSC and provides a conceptually simple interpretation from a bilateral variance estimation perspective, namely that singular-value decomposition of similar packed patches can be viewed as pooling both local and nonlocal information for estimating signal variances.
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Compressive Sensing via Nonlocal Low-Rank Regularization
TL;DR: A nonlocal low-rank regularization approach toward exploiting structured sparsity and its application into CS of both photographic and MRI images is proposed and the use of a nonconvex log det as a smooth surrogate function for the rank instead of the convex nuclear norm is proposed.