| 基于联合稀疏近似的彩色图像超分辨率重建 |
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| 引用本文： | 李民,程建,乐翔,李小文.基于联合稀疏近似的彩色图像超分辨率重建[J].光电子．激光,2011(8):1241-1245. |
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| 作者姓名： | 李民 程建 乐翔 李小文 |
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| 作者单位： | 电子科技大学地表空间信息技术研究所；;电子科技大学电子工程学院；;电子科技大学电子工程学院；;电子科技大学地表空间信息技术研究所； |
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| 基金项目： | 国家“973”计划资助项目(2007CB714406);中国博士后基金资助项目(20080441198);电子科技大学青年科技基金重点资助项目(JX0804) |
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| 摘 要： | 针对常用的超分辨率（SR）算法中彩色图像的处理会出现彩色信息的丢失或处理结果色彩偏差较大的问题，提出基于联合稀疏近似（SSA）的彩色图像SR重建方法（SR-SSA）。将多通道数据进行联合稀疏编码（SC），并保证它们具有相同的稀疏性模式；同时考虑了彩色图像的各通道数据，并兼顾了它们之间的相关性，增强了先验知识的表达能力。...
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| 关 键 词： | 彩色图像处理 基于学习的超分辨率(sR) 联合稀疏近似(SSA) 稀疏性模式 lp q范数 |
Super-resolution reconstruction for color images based on simultaneous sparse approximation |
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| LI Min,CHENG Jian,LE Xiang and LI Xiao-wen.Super-resolution reconstruction for color images based on simultaneous sparse approximation[J].Journal of Optoelectronics·laser,2011(8):1241-1245. |
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| Authors: | LI Min CHENG Jian LE Xiang and LI Xiao-wen |
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| Affiliation: | Institute of Geo-Spatial Information Science and Technology,University of Electronic Science and Technology of China,Chengdu 631731,China;;School of Electronic Engineering,University of Electronic Science and Technology of China,Chengdu 611731 China;3.Department of Scientific Research,Guilin Airforce Academy,Guilin 541003,China;School of Electronic Engineering,University of Electronic Science and Technology of China,Chengdu 611731 China;3.Department of Scientific Research,Guilin Airforce Academy,Guilin 541003,China;Institute of Geo-Spatial Information Science and Technology,University of Electronic Science and Technology of China,Chengdu 631731,China; |
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| Abstract: | In this paper,a novel super-resolution(SR) method for color images based on simultaneous sparse approximation(SSA) is presented to overcome the problems of color information loss and perceptible color deviation in common super-resolution(SR) reconstruction methods.The multichannel data is unified for simultaneous sparse coding to make sure the same sparsity patterns.The expression ability of prior information is enhanced because of considering all channels and the correlation among them.Our meth... |
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| Keywords: | color image processing learning-based super-resolution(SR) simultaneous sparse approximation(SSA) sparsity pattern lp q-norm |
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