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| Super-resolution Reconstruction Algorithm Based on Non-local Simultaneous Sparse Approximation |
| Li Min①③ Li Shi-hua① Li Xiao-wen① Le Xiang② |
①(Institute of Geo-Spatial Information Science and Technology,University of Electronic Science and Technology of China, Chengdu 611731, China)
②(School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)
③(Department of Scientific Research, Guilin Airforce Academy, Guilin 541003, China) |
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Abstract A novel super-resolution reconstruction method based on non-local simultaneous sparse approximation is presented, which combines simultaneous sparse approximation method and non-local self-similarity. The sparse association between high- and low-resolution patches pairs of cross-scale self-similar sets via simultaneous sparse coding is defined, and the association as a priori knowledge is used for super-resolution reconstruction. This method keeps the patches pairs the same sparsity patterns, and makes efficiently use of the self-similar information. The adaptability is enhanced. Several experiments using nature images show that the presented method outperforms other several learning-based super-resolution methods.
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Received: 11 October 2010
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Corresponding Authors:
Li Min
E-mail: gllm126@163.com
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2011, Vol. 33 
