image super-resolution based on sparse representation via direction and edge dictionaries
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ID: 152075
2017
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Abstract
Sparse representation has recently attracted enormous interests in the field of image super-resolution. The sparsity-based methods usually train a pair of global dictionaries. However, only a pair of global dictionaries cannot best sparsely represent different kinds of image patches, as it neglects two most important image features: edge and direction. In this paper, we propose to train two novel pairs of Direction and Edge dictionaries for super-resolution. For single-image super-resolution, the training image patches are, respectively, divided into two clusters by two new templates representing direction and edge features. For each cluster, a pair of Direction and Edge dictionaries is learned. Sparse coding is combined with the Direction and Edge dictionaries to realize super-resolution. The above single-image super-resolution can restore the faithful high-frequency details, and the POCS is convenient for incorporating any kind of constraints or priors. Therefore, we combine the two methods to realize multiframe super-resolution. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed method. Experimental results demonstrate that our method can recover better edge structure and details.
| Reference Key |
zhu2017mathematicalimage
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|---|---|
| Authors | ;Xuan Zhu;Xianxian Wang;Jun Wang;Peng Jin;Li Liu;Dongfeng Mei |
| Journal | journal of power sources |
| Year | 2017 |
| DOI |
10.1155/2017/3259357
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| URL | |
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