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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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Proceedings ArticleDOI
Sparsity-based image denoising via dictionary learning and structural clustering
TL;DR: A double-header l1-optimization problem where the regularization involves both dictionary learning and structural structuring is formulated and a new denoising algorithm built upon clustering-based sparse representation (CSR) is proposed.
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Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation
TL;DR: A new hyperspectral image super-resolution method from a low-resolution (LR) image and a HR reference image of the same scene to improve the accuracy of non-negative sparse coding and to exploit the spatial correlation among the learned sparse codes.
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Denoising Prior Driven Deep Neural Network for Image Restoration
TL;DR: Zhang et al. as mentioned in this paper proposed a convolutional neural network (CNN) based denoiser that can exploit the multi-scale redundancies of natural images and leverages the prior of the observation model.
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Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling
TL;DR: This paper incorporates the image nonlocal self-similarity into SRM for image interpolation, and shows that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective forimage interpolation.
Proceedings ArticleDOI
Centralized sparse representation for image restoration
TL;DR: A novel sparse representation model called centralized sparse representation (CSR) is proposed, which achieves convincing improvement over previous state-of-the-art methods on image restoration tasks by exploiting the nonlocal image statistics.