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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.
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Received: 03 July 2013
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2014, Vol. 27 
