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题名:
Image super-resolution reconstruction via EROMP sparse representation
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
文章类型: 期刊论文
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
内容类型: 期刊论文
URI标识: http://ir.ioe.ac.cn/handle/181551/5029
Appears in Collections:光电探测与信号处理研究室(五室)_期刊论文

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作者单位: 中国科学院光电技术研究所

Recommended Citation:
Jinzheng Lua,Qiheng Zhanga,Zhiyong Xua,et al. Image super-resolution reconstruction via EROMP sparse representation[J]. Procedia Engineering,2011,15:1524-1528.
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