杨晓军,
张静,
孙超,
张天骐
1.重庆邮电大学通信与信息工程学院 重庆 400065
2.重庆邮电大学信号与信息处理重庆市重点实验室 重庆 400065
基金项目:国家自然科学基金(61671095)
详细信息
作者简介:赵辉：女，1980年生，教授，硕士生导师，研究方向为信号与图像处理
杨晓军：男，1994年生，硕士生，研究方向为信号与图像处理
张静：女，1992年生，硕士生，研究方向为信号与图像处理
孙超：男，1992年生，硕士生，研究方向为信号与图像处理
张天骐：男，1971年生，博士后，教授，研究方向为通信信号的调制解调、盲处理、语音信号处理
通讯作者:赵辉 zhaohui@cqupt.edu.cn
中图分类号:TN911.73; TP391计量
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被引次数:0
出版历程
收稿日期:2019-04-11
修回日期:2020-03-07
网络出版日期:2020-04-09
刊出日期:2020-11-16
Image Compressed Sensing Reconstruction Based on Structural Group Total Variation
Hui ZHAO,,Xiaojun YANG,
Jing ZHANG,
Chao SUN,
Tianqi ZHANG
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (61671095)
摘要
摘要:针对基于传统全变分(TV)模型的图像压缩感知(CS)重建算法不能有效地恢复图像的细节和纹理，从而导致图像过平滑的问题，该文提出一种基于结构组全变分(SGTV)模型的图像压缩感知重建算法。该算法利用图像的非局部自相似性和结构稀疏特性，将图像的重建问题转化为由非局部自相似图像块构建的结构组全变分最小化问题。算法以结构组全变分模型为正则化约束项构建优化模型，利用分裂Bregman迭代将算法分离成多个子问题，并对每个子问题高效地求解。所提算法很好地利用了图像自身的信息和结构稀疏特性，保护了图像细节和纹理。实验结果表明，该文所提出的算法优于现有基于全变分模型的压缩感知重建算法，在PSNR和视觉效果方面取得了显著提升。
关键词:图像重建/
压缩感知/
非局部自相似/
全变分
Abstract:To solve the problem that the traditional Compressed Sensing (CS) algorithm based on Total Variation (TV) model can not effectively restore details and texture of image, which leads to over-smoothing of reconstructed image, an image Compressed Sensing (CS) reconstruction algorithm based on Structural Group TV (SGTV) model is proposed. The proposed algorithm utilizes the non-local self-similarity and structural sparsity of image, and converts the CS recovery problem into the total variation minimization problem of the structural group constructed by non-local self-similar image blocks. In addition, the optimization model of the proposed algorithm is built with regularization constraint of the structural group total variation model, and it uses the split Bregman iterative algorithm to separate it into multiple sub-problems, and then solves them respectively. The proposed algorithm makes full use of the information and structural sparsity of image to protects the image details and texture. The experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art total variation based algorithm in both PSNR and visual perception.
Key words:Image reconstruction/
Compressed Sensing (CS)/
Nonlocal self-similarity/
Total Variation (TV)
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