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Proceedings Paper

Super-resolution with nonlocal regularized sparse representation
Author(s): Weisheng Dong; Guangming Shi; Lei Zhang; Xiaolin Wu
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Paper 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.

Paper Details

Date Published: 15 July 2010
PDF: 10 pages
Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77440H (15 July 2010); doi: 10.1117/12.863368
Show Author Affiliations
Weisheng Dong, Xidian Univ. (China)
Guangming Shi, Xidian Univ. (China)
Lei Zhang, The Hong Kong Polytechnic Univ. (Hong Kong, China)
Xiaolin Wu, McMaster Univ. (Canada)


Published in SPIE Proceedings Vol. 7744:
Visual Communications and Image Processing 2010
Pascal Frossard; Houqiang Li; Feng Wu; Bernd Girod; Shipeng Li; Guo Wei, Editor(s)

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