Image super-resolution reconstruction via EROMP sparse representation
文献类型：期刊论文
| 作者 | Jinzheng Lua; Qiheng Zhanga; Zhiyong Xua; Zhenming Peng |
| 刊名 | Procedia Engineering
 |
| 出版日期 | 2011 |
| 卷号 | 15页码:1524-1528 |
| 通讯作者 | Jinzheng Lua |
| 中文摘要 | In order to improve the resolution of single-image, a new super-resolution reconstruction method is proposed using sparse representation via enhanced regularized-orthogonal-matching-pursuit. The core task of the SR problem is to solve the basis representation of image patches with respect to corresponding over-complete dictionary. Since the guarantee and the speed of a coding algorithm are very important in both dictionary learning and signal decomposition, we present a rapid sparse representation algorithm. Moreover, only low resolution dictionary is learned from image examples for reducing time consumption of dictionary learning. And the correspondence of high resolution is obtained under the numerical calculation. Experimental results show that the proposed method can effectively improve image resolution. The peak signal to noise ratio and structural similarity are gained 2.1 dB and 0.09 respectively, compared with Bicubic interpolation widely used. |
| 英文摘要 | In order to improve the resolution of single-image, a new super-resolution reconstruction method is proposed using sparse representation via enhanced regularized-orthogonal-matching-pursuit. The core task of the SR problem is to solve the basis representation of image patches with respect to corresponding over-complete dictionary. Since the guarantee and the speed of a coding algorithm are very important in both dictionary learning and signal decomposition, we present a rapid sparse representation algorithm. Moreover, only low resolution dictionary is learned from image examples for reducing time consumption of dictionary learning. And the correspondence of high resolution is obtained under the numerical calculation. Experimental results show that the proposed method can effectively improve image resolution. The peak signal to noise ratio and structural similarity are gained 2.1 dB and 0.09 respectively, compared with Bicubic interpolation widely used. |
| 语种 | 英语 |
| 源URL | [http://ir.ioe.ac.cn/handle/181551/5029]  |
| 专题 | 光电技术研究所_光电探测与信号处理研究室（五室） |
| 作者单位 | 中国科学院光电技术研究所 |
| 推荐引用方式 GB/T 7714 | Jinzheng Lua,Qiheng Zhanga,Zhiyong Xua,et al. Image super-resolution reconstruction via EROMP sparse representation[J]. Procedia Engineering,2011,15:1524-1528. |
| APA | Jinzheng Lua,Qiheng Zhanga,Zhiyong Xua,&Zhenming Peng.(2011).Image super-resolution reconstruction via EROMP sparse representation.Procedia Engineering,15,1524-1528. |
| MLA | Jinzheng Lua,et al."Image super-resolution reconstruction via EROMP sparse representation".Procedia Engineering 15(2011):1524-1528. |
入库方式： OAI收割
来源：光电技术研究所
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