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- Title
Image super-resolution based on sparse representation and self-similarity learning.
- Authors
LI Qiang; LIN Wen-xiao
- Abstract
The reconstructed image quality of super-resolution based on sparse representation depends on the information of high-resolution image database, the result can not be guaranteed. Super-resolution based on image structural similarity only uses the additional information contained in the given low-resolution image itself, but the information is not enough to get an ideal reconstructed image. In order to use both training database and the given low-resolution image, a new method is put forward in this paper. First,use the sparse representation based algorithm to reconstruct the image; and then, use the additional information contained in the low resolution image to repair the achieved image in the first step, enhance the quality in advance. The simulation result indicates that the method perform better than the - two algorithm mentioned above.
- Subjects
HIGH resolution imaging; IMAGE quality in imaging systems; SELF-similar processes; SPARSE approximations; IMAGE databases
- Publication
Basic Sciences Journal of Textile Universities / Fangzhi Gaoxiao Jichu Kexue Xuebao, 2013, Vol 26, Issue 4, p548
- ISSN
1006-8341
- Publication type
Article