Learning-Based Image Super-Resolution with Directional Total Variation | ||||
| The International Conference on Electrical Engineering | ||||
| Article 64, Volume 7, 7th International Conference on Electrical Engineering ICEENG 2010, May 2010, Page 1-11 PDF (186.21 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iceeng.2010.33038 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Osama. A. Omer | ||||
| Department of Electrical Engineering, South Valley University, Aswan. | ||||
| Abstract | ||||
| Abstract: We propose a super-resolution algorithm based on local adaptation. In the proposed algorithm, the mapping function from the low-resolution images to high-resolution image is estimated by adaptation. Moreover, the property of the high-resolution image is learned and incorporated in a regularization-based restoration. The proposed regularization function is used as a general directional total variation with adaptive weights. The adaptive weights of the directional total variation are estimated based on the property of the partially reconstructed high-resolution image. The regularization function can be thought as a linear combination of smoothness in different directions. The convexity conditions as well as the convergence conditions are studied for the proposed algorithm. | ||||
| Keywords | ||||
| Super-resolution; image fusion; Restoration; directional total variation; regularization | ||||
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