Guangming Shi

Publications391
h-index40
Citations9,857
Highly Influential Citations841
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Nonlocally Centralized Sparse Representation for Image Restoration
TLDR
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. Expand
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
TLDR
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. Expand
Two-stage image denoising by principal component analysis with local pixel grouping
TLDR
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. Expand
Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach
  • W. Dong, Guangming Shi, Xin Li
  • Mathematics, Medicine
  • IEEE Transactions on Image Processing
  • 1 February 2013
TLDR
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. Expand
Compressive Sensing via Nonlocal Low-Rank Regularization
TLDR
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. Expand
Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling
TLDR
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. Expand
Sparsity-based image denoising via dictionary learning and structural clustering
TLDR
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. Expand
Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation
  • W. Dong, Fazuo Fu, +4 authors Xin Li
  • Computer Science, Medicine
  • IEEE Transactions on Image Processing
  • 22 March 2016
TLDR
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. Expand
Centralized sparse representation for image restoration
TLDR
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. Expand
Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation.
TLDR
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. Expand
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