基于组稀疏表示和加权全变分的图像压缩感知重构
TP391; 传统的基于组稀疏表示(group sparse representation,GSR)的压缩感知(compressd sensing,CS)重构算法利用信号的稀疏性和非局部相似性来重构图像信号,但没有充分考虑图像的局部平滑特性,影响了算法的重构性能.考虑信号的稀疏性、非局部相似性、平滑性3种先验信息,提出一种基于GSR和加权全变分(weighted total variation,WTV)的图像CS重构算法,并针对传统的WTV采用全局加权会引入错误的纹理以及边缘状伪影的问题,利用一种新的WTV策略,只对图像的高频分量设置权重来保证图像重构质量.此外,针对硬阈值迭代法忽略低频的主分...
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| Published in | 系统工程与电子技术 Vol. 42; no. 10; pp. 2172 - 2180 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
重庆邮电大学通信与信息工程学院,重庆400065
01.10.2020
重庆邮电大学信号与信息处理重庆市重点实验室,重庆400065 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-506X |
| DOI | 10.3969/j.issn.1001-506X.2020.10.04 |
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| Abstract | TP391; 传统的基于组稀疏表示(group sparse representation,GSR)的压缩感知(compressd sensing,CS)重构算法利用信号的稀疏性和非局部相似性来重构图像信号,但没有充分考虑图像的局部平滑特性,影响了算法的重构性能.考虑信号的稀疏性、非局部相似性、平滑性3种先验信息,提出一种基于GSR和加权全变分(weighted total variation,WTV)的图像CS重构算法,并针对传统的WTV采用全局加权会引入错误的纹理以及边缘状伪影的问题,利用一种新的WTV策略,只对图像的高频分量设置权重来保证图像重构质量.此外,针对硬阈值迭代法忽略低频的主分量系数,采用硬阈值-模平方方法来更好地保护非主分量系数.实验表明,相同采样率下,所提算法的峰值信噪比比非局部正则化全变分和基于GSR的CS算法平均分别提高5.4 dB和0.62 dB,验证了所提算法有效保护图像的细节信息. |
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| AbstractList | TP391; 传统的基于组稀疏表示(group sparse representation,GSR)的压缩感知(compressd sensing,CS)重构算法利用信号的稀疏性和非局部相似性来重构图像信号,但没有充分考虑图像的局部平滑特性,影响了算法的重构性能.考虑信号的稀疏性、非局部相似性、平滑性3种先验信息,提出一种基于GSR和加权全变分(weighted total variation,WTV)的图像CS重构算法,并针对传统的WTV采用全局加权会引入错误的纹理以及边缘状伪影的问题,利用一种新的WTV策略,只对图像的高频分量设置权重来保证图像重构质量.此外,针对硬阈值迭代法忽略低频的主分量系数,采用硬阈值-模平方方法来更好地保护非主分量系数.实验表明,相同采样率下,所提算法的峰值信噪比比非局部正则化全变分和基于GSR的CS算法平均分别提高5.4 dB和0.62 dB,验证了所提算法有效保护图像的细节信息. |
| Author | 李志伟 方禄发 赵辉 徐先明 张天骐 |
| AuthorAffiliation | 重庆邮电大学通信与信息工程学院,重庆400065;重庆邮电大学信号与信息处理重庆市重点实验室,重庆400065 |
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| Author_FL | FANG Lufa ZHANG Tianqi XU Xianming ZHAO Hui LI Zhiwei |
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| Keywords | 加权全变分 图像重构 组稀疏表示 压缩感知 |
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| Title | 基于组稀疏表示和加权全变分的图像压缩感知重构 |
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