张静,
张乐,
刘莹莉,
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
张静：女，1992年生，硕士生，研究方向为信号与图像处理
张乐：女，1993年生，硕士生，研究方向为信号与图像处理
刘莹莉：女，1994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、神经网络实现以及FPGA, VLSI实现
通讯作者:赵辉 zhaohui@cqupt.edu.cn
中图分类号:TP391.41计量
文章访问数:2372
HTML全文浏览量:826
PDF下载量:99
被引次数:0
出版历程
收稿日期:2018-08-22
修回日期:2019-01-28
网络出版日期:2019-02-25
刊出日期:2019-08-01
Compressed Sensing Image Restoration Based on Non-local Low Rank and Weighted Total Variation
Hui ZHAO,,Jing ZHANG,
Le ZHANG,
Yingli LIU,
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:为准确有效地实现自然图像的压缩感知(CS)重构，该文提出一种基于图像非局部低秩(NLR)和加权全变分(WTV)的CS重构算法。该算法考虑图像的非局部自相似性(NSS)和局部光滑特性，对传统的全变分(TV)模型进行改进，只对图像的高频分量设置权重，并用一种差分曲率的边缘检测算子来构造权重系数。此外，算法以改进的TV模型与NLR模型为约束构建优化模型，并分别采用光滑非凸函数和软阈值函数来求解低秩和全变分优化问题，很好地利用了图像的自身性质，保护了图像的细节信息，并提高了算法的抗噪性和适应性。仿真结果表明，与基于NLR的CS算法相比，相同采样率下，该文所提算法的峰值信噪比最高可提高2.49 dB，且抗噪性更强，验证了算法的有效性。
关键词:压缩感知/
图像重构/
非局部低秩/
加权全变分
Abstract:In order to reconstruct natural image from Compressed Sensing(CS) measurements accurately and effectively, a CS image reconstruction algorithm based on Non-local Low Rank(NLR) and Weighted Total Variation(WTV) is proposed. The proposed algorithm considers the Non-local Self-Similarity(NSS) and local smoothness in the image and improves the traditional TV model, in which only the weights of image’s high-frequency components are set and constructed with a differential curvature edge detection operator. Besides, the optimization model of the proposed algorithm is built with constraints of the improved TV and the non-local low rank model, and a non-convex smooth function and a soft thresholding function are utilized to solve low rank and TV optimization problems respectively. By taking advantage of them, the proposed method makes full use of the property of image, and therefore conserves the details of image and is more robust and adaptable. Experimental results show that, compared with the CS reconstruction algorithm via non-local low rank, at the same sampling rate, the Peak Signal to Noise Ratio(PSNR) of the proposed method increases by 2.49 dB at most and the proposed method is more robust, which proves the effectiveness of the proposed algorithm.
Key words:Compressed Sensing(CS)/
Image reconstruction/
Non-local Low Rank(NLR)/
Weighted Total Variation(WTV)
PDF全文下载地址:
https://jeit.ac.cn/article/exportPdf?id=4ea00e70-4e1b-406e-b51d-bd165c2e8354
