模式识别与人工智能
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  2014, Vol. 27 Issue (9): 787-793    DOI:
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Super-Resolution Image Reconstruction Based on Local Structural Similarity and Collaborative Representation
CAI Miao-Miao, TAN Yuan-Peng, CAO Fei-Long
Department of Information and Mathematical Sciences, China Jiliang University, Hangzhou 310018

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Abstract  An approach for super-resolution image reconstruction is presented based on local structural similarity and collaborative representation. The collaborative representation l2-norm regularization and local similarity constraint are employed to seek a linear combination for a patch of low-resolution input image with respect to the low-resolution dictionary. Then, the high-resolution image patch is generated by virtue of the coefficients of this combination and the high-resolution dictionary. In addition, the l2-norm based objective function implies an analytical solution and it does not involve local minima. Hence, it performs at a lower complexity compared to l1-sparsity constraint model. The experimental results demonstrate that the proposed method is feasible and effective for small super-resolution image reconstruction and outperforms the bicubic interpolation method and sparse representation super-resolution model on both visual effect and numerical results. Further research shows that the proposed method also performs well for large magnification factors and noisy data.
Key wordsLocal Structural Similarity      Collaborative Representation      Sparse Representation      Super-Resolution Image      Image Reconstruction     
Received: 03 July 2013     
ZTFLH: TP391  
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CAI Miao-Miao
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CAO Fei-Long
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CAI Miao-Miao,TAN Yuan-Peng,CAO Fei-Long. Super-Resolution Image Reconstruction Based on Local Structural Similarity and Collaborative Representation[J]. , 2014, 27(9): 787-793.
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http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2014/V27/I9/787
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