结合自适应稀疏表示和全变分约束的图像重建

doi:10.3969/j.issn.1001-2400.2016.01.003

J4 ›› 2016, Vol. 43 ›› Issue (1): 12-17+109.doi: 10.3969/j.issn.1001-2400.2016.01.003

结合自适应稀疏表示和全变分约束的图像重建

王勇1;冯唐智1;陈楚楚1;乔倩倩1;杨笑宇1;王国栋1;高全学2

(1. 西安电子科技大学电子工程学院，陕西西安 710071;
2. 西安电子科技大学通信工程学院，陕西西安 710071)

收稿日期:2014-07-29 出版日期:2016-02-20 发布日期:2016-04-06
通讯作者: 王勇
作者简介:王勇(1976-)，男，副教授，E-mail：yongwang@126.com．
基金资助:
国家自然科学基金资助项目(61271296)；中央高校基本科研业务费专项资金资助项目(JB150218)；西安电子科技大学教育教学改革研究资助项目(B1311)；西安电子科技大学新实验开发与新实验设备研制及实验教学改革资助项目(SY1354)

Adaptive sparse representation and total variation constraint based image reconstruction

WANG Yong1;FENG Tangzhi1;CHEN Chuchu1;QIAO Qianqian1;YANG Xiaoyu1;WANG Guodong1;GAO Quanxue2

(1. School of Electronic Engineering, Xidian Univ., Xi'an 710071, China;
2. School of Telecommunication Engineering, Xidian Univ., Xi'an 710071, China)

Received:2014-07-29 Online:2016-02-20 Published:2016-04-06
Contact: WANG Yong

摘要/Abstract

摘要：

针对以二维小波变换和离散余弦变换为代表的固定正交基在图像压缩感知高分辨率重建中的局限性，提出了一种新的自适应冗余字典稀疏表示结合全变分约束的图像高分辨率重建算法．该算法以迭代过程的中间图像作为训练样本，通过自适应学习获得适合样本特征的冗余字典，它充分利用了字典原子与待重建图像的相关性，获得了待重建图像的理想完备稀疏表示，从而降低了采样率，提高了图像重建质量．最后，以全变分作为正则化条件，采用交替迭代算法求解稀疏优化问题．仿真结果表明，该算法可以在低采样率下重建出高质量的图像．

关键词: 压缩感知, 自适应冗余字典, 稀疏表示, 图像重建, 全变分

Abstract:

In view of the limitation of fixed complete orthogonal transformation, represented by two-dimensional wavelet transform and discrete cosine transform in compressed sensing high-resolution image reconstruction, this paper proposes a new method for high-resolution image reconstruction based on adaptive redundant dictionary sparse representation with the total variation constraint.The algorithm takes the intermediate image in the process of iteration as the training sample to get a redundant dictionary suitable for sample characteristics by adaptive learning. It makes full use of the correlation between dictionary atoms and the image to get an ideal complete sparse representation, thus reducing the sampling rate and improving the quality of image reconstruction. Finally, the algorithm takes the total variation as a constraint and uses the split Bregman iterative method to solve the sparse optimization problem. Simulation shows that the proposed method can reconstruct high quality images under a low sampling rate.

Key words: compressed sensing, adaptive redundant dictionary, sparse representation, image reconstruction, total variation

王勇;冯唐智;陈楚楚;乔倩倩;杨笑宇;王国栋;高全学. 结合自适应稀疏表示和全变分约束的图像重建[J]. J4, 2016, 43(1): 12-17+109.

WANG Yong;FENG Tangzhi;CHEN Chuchu;QIAO Qianqian;YANG Xiaoyu;WANG Guodong. Adaptive sparse representation and total variation constraint based image reconstruction[J]. J4, 2016, 43(1): 12-17+109.

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结合自适应稀疏表示和全变分约束的图像重建

Adaptive sparse representation and total variation constraint based image reconstruction

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