| Image super-resolution enhancement based on online learning and blind sparse decomposition | |
| Jinzheng Lu; Qiheng Zhang; Zhiyong Xu; Zhenming Peng | |
| Volume | 8004 |
| Pages | 80040B |
| 2011 | |
| Language | 英语 |
| Indexed By | Ei |
| Subtype | 会议论文 |
| Abstract | This paper presents a different learning-based image super-resolution enhancement method based on blind sparse decomposition, in order to improve its resolution of a degraded one. Firstly, sparse decomposition based image super-resolution enhancement model is put forward according to the geometrical invariability of local image structures under different conditions of resolution. Secondly, for reducing the complexity of dictionary learning and enhancing adaptive representation ability of dictionary atoms, the over-complete dictionary is constructed using online learning fashion of the given low resolution image. Thirdly, since the fixed sparsity of the conventional matching pursuit algorithms for sparse decomposition can not fit all types of patches, the approach to sparse decomposition with blind sparsity can achieve relatively higher accurate sparse representation of an image patch. Lastly, atoms of high resolution dictionary and coefficients of representation of the given low-resolution image are synthesized to the desired SR image. Experimental results of the synthetic and real data demonstrate that the suggested framework can eliminate blurring degradation and annoying edge artifacts in the resulting images. The proposed method can be effectively applied to resolution enhancement of the single-frame low-resolution image.; This paper presents a different learning-based image super-resolution enhancement method based on blind sparse decomposition, in order to improve its resolution of a degraded one. Firstly, sparse decomposition based image super-resolution enhancement model is put forward according to the geometrical invariability of local image structures under different conditions of resolution. Secondly, for reducing the complexity of dictionary learning and enhancing adaptive representation ability of dictionary atoms, the over-complete dictionary is constructed using online learning fashion of the given low resolution image. Thirdly, since the fixed sparsity of the conventional matching pursuit algorithms for sparse decomposition can not fit all types of patches, the approach to sparse decomposition with blind sparsity can achieve relatively higher accurate sparse representation of an image patch. Lastly, atoms of high resolution dictionary and coefficients of representation of the given low-resolution image are synthesized to the desired SR image. Experimental results of the synthetic and real data demonstrate that the suggested framework can eliminate blurring degradation and annoying edge artifacts in the resulting images. The proposed method can be effectively applied to resolution enhancement of the single-frame low-resolution image. |
| Conference Name | Proc. of SPIE |
| Conference Date | 2011 |
| Document Type | 会议论文 |
| Identifier | http://ir.ioe.ac.cn/handle/181551/7688 |
| Collection | 光电探测与信号处理研究室（五室） |
| Corresponding Author | Jinzheng Lu |
| Affiliation | 中国科学院光电技术研究所 |
| Recommended Citation GB/T 7714 | Jinzheng Lu,Qiheng Zhang,Zhiyong Xu,et al. Image super-resolution enhancement based on online learning and blind sparse decomposition[C],2011:80040B. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 2011-05-004.pdf（567KB） | 会议论文 | 开放获取 | CC BY-NC-SA | Application Full Text | ||
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