14 July 2010 Super-resolution with nonlocal regularized sparse representation
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Proceedings Volume 7744, Visual Communications and Image Processing 2010; 77440H (2010) https://doi.org/10.1117/12.863368
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China
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.
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Weisheng Dong, Guangming Shi, Lei Zhang, Xiaolin Wu, "Super-resolution with nonlocal regularized sparse representation", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77440H (14 July 2010); doi: 10.1117/12.863368; https://doi.org/10.1117/12.863368
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