Example-Based Super-Resolution
IEEE Computer Graphics and Applications
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Learning non-local range Markov Random field for image restoration
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Internal statistics of a single natural image
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
From learning models of natural image patches to whole image restoration
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Centralized sparse representation for image restoration
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Image nonlocal self-similarity has been widely adopted as natural image prior in various low-level vision tasks such as image restoration, while the low-rank matrix recovery theory has been drawing much attention to describe and utilize the image nonlocal self-similarities. However, whether the low-rank prior models exist to characterize the nonlocal self-similarity for a wide range of natural images is not clear yet. In this paper we investigate this issue by evaluating the heavy-tailed distributions of singular values of the matrices of nonlocal similar patches collected from natural images. A novel image prior model, namely nonlocal spectral prior (NSP) model, is then proposed to characterize the singular values of nonlocal similar patches. We consequently apply the NSP model to typical image restoration tasks, including denoising, super-resolution and deblurring, and the experimental results demonstrated the highly competitive performance of NSP in solving these low-level vision problems.