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Image super-resolution based on multi-space sparse representation
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AI
DOI：10.1007/s11042-011-0953-4.png)
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Abstract
En 中文
Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.
Keywords:
Super-resolution
Sparse representation
Total variation
Over-complete bases
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W