基于非局部相似模型的压缩感知图像恢复算法

沈燕飞; 李锦涛; 朱珍民; 张勇东; 代锋

doi:10.16383/j.aas.2015.c140210

基于非局部相似模型的压缩感知图像恢复算法

doi: 10.16383/j.aas.2015.c140210

沈燕飞^1,2, ,,
李锦涛^1,2,
朱珍民^1,2,
张勇东^1,2,
代锋^1,2

1.
中国科学院计算技术研究所北京 100190;
2.
北京市移动计算与新型终端重点实验室北京 100190

基金项目:

国家自然科学基金(61327013,61471343),中国科学院科研装备研制项目(YZ201321)资助

详细信息

作者简介:
李锦涛中国科学院计算技术研究所研究员. 主要研究方向为多媒体技术, 虚拟现实技术与普适计算技术.E-mail: jtli@ict.ac.cn

通讯作者:
沈燕飞中国科学院计算技术研究所副研究员. 2014 年获得中国科学院大学博士学位. 主要研究方向为图像处理. 本文通信作者. E-mail: syf@ict.ac.cn

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出版历程
- 收稿日期: 2014-04-01
- 修回日期: 2014-08-12
- 刊出日期: 2015-02-20

Image Reconstruction Algorithm of Compressed Sensing Based on Nonlocal Similarity Model

SHEN Yan-Fei^{1,2
, ,},
LI Jin-Tao^1,2,
ZHU Zhen-Min^1,2,
ZHANG Yong-Dong^1,2,
DAI Feng^1,2

1.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190;
2.
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190

Funds:

Supported by National Natural Science Foundation of China (61327013, 61471343) and Instrument Developing Project of the Chinese Academy of Sciences (YZ201321)

摘要

摘要: 针对压缩感知(Compressed sensing, CS)图像恢复问题, 提出了一种基于非局部相似模型的压缩感知恢复算法, 该算法将传统意义上二维图像块的稀疏性扩展到相似图像块组在三维空间上的稀疏性, 在提高图像表示稀疏度的同时进一步提高了压缩感知图像恢复效率, 恢复图像在纹理和结构保持方面都得到了很大的提升. 在该算法模型求解过程中, 使用增广拉格朗日方法将受限优化问题转换为非受限优化问题, 为减少计算复杂度, 还使用了基于泰勒展开的线性化技术来加速算法求解. 实验结果表明, 该算法的图像恢复性能优于目前主流的压缩感知图像恢复算法.
- 压缩感知 /
- 图像恢复 /
- 非局部相似 /
- 稀疏表示
Abstract: In this paper, an image reconstruction algorithm of compressed sensing (CS) is proposed based on nonlocal similarity model. Instead of using the traditional sparse property of 2D image blocks, the sparse representation of 3D similar image block group is exploited to increase the sparse degree of reconstructed image and improve the performance of the compressed sensing reconstruction algorithm. The texture and structure features are well preserved in the reconstructed image. In the solution of our proposed algorithm, the constrained optimization problem is transformed into an unconstrained optimization problem by augmented Lagrangian method, and the linear technique, which is based on Taylor expansion, is employed to reduce the computational burden and accelerates our proposed algorithm. Experimental results show that the subjective and objective performance of our proposed reconstruction algorithm is superior to the state of art reconstruction algorithms.
- Compressed sensing (CS) /
- image reconstruction /
- nonlocal similarity /
- sparse representation

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参考文献(38)

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基于非局部相似模型的压缩感知图像恢复算法

doi: 10.16383/j.aas.2015.c140210

作者简介:
李锦涛中国科学院计算技术研究所研究员. 主要研究方向为多媒体技术, 虚拟现实技术与普适计算技术.E-mail: jtli@ict.ac.cn

通讯作者:
沈燕飞中国科学院计算技术研究所副研究员. 2014 年获得中国科学院大学博士学位. 主要研究方向为图像处理. 本文通信作者. E-mail: syf@ict.ac.cn

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Image Reconstruction Algorithm of Compressed Sensing Based on Nonlocal Similarity Model

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留言板

基于非局部相似模型的压缩感知图像恢复算法

doi: 10.16383/j.aas.2015.c140210

作者简介: 李锦涛 中国科学院计算技术研究所研究员. 主要研究方向为多媒体技术, 虚拟现实技术与普适计算技术.E-mail: jtli@ict.ac.cn

通讯作者: 沈燕飞 中国科学院计算技术研究所副研究员. 2014 年获得中国科学院大学博士学位. 主要研究方向为图像处理. 本文通信作者. E-mail: syf@ict.ac.cn

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出版历程

Image Reconstruction Algorithm of Compressed Sensing Based on Nonlocal Similarity Model

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出版历程

目录

作者简介:
李锦涛中国科学院计算技术研究所研究员. 主要研究方向为多媒体技术, 虚拟现实技术与普适计算技术.E-mail: jtli@ict.ac.cn

通讯作者:
沈燕飞中国科学院计算技术研究所副研究员. 2014 年获得中国科学院大学博士学位. 主要研究方向为图像处理. 本文通信作者. E-mail: syf@ict.ac.cn