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Title: Super-resolution with nonlocal regularized sparse representation
Authors: Dong, W
Shi, G
Zhang, L 
Wu, X
Keywords: Super resolution
Inverse problems
Principal component analysis
Issue Date: 2010
Publisher: SPIE-International Society for Optical Engineering
Source: Proceedings of SPIE : the International Society for Optical Engineering, 2010, v. 7744, 77440H How to cite?
Journal: Proceedings of SPIE : the International Society for Optical Engineering 
Abstract: The reconstruction of a high resolution (HR) image from its low resolution (LR) counterpart is a challenging problem. The recently developed sparse representation (SR) techniques provide new solutions to this inverse problem by introducing the l1-norm sparsity prior into the super-resolution reconstruction process. In this paper, we present a new SR based image super-resolution by optimizing the objective function under an adaptive sparse domain and with the nonlocal regularization of the HR images. The adaptive sparse domain is estimated by applying principal component analysis to the grouped nonlocal similar image patches. The proposed objective function with nonlocal regularization can be efficiently solved by an iterative shrinkage algorithm. The experiments on natural images show that the proposed method can reconstruct HR images with sharp edges from degraded LR images.
Description: Conference on Visual Communications and Image Processing 2010, Huang Shan, China, 11-14 July, 2010
ISSN: 0277-786X
EISSN: 1996-756X
DOI: 10.1117/12.863368
Appears in Collections:Conference Paper

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