一种有效的压缩感知图像重建算法  

An Efficient Algorithm for Compressed Sensing Image Reconstruction

作　　者：李志林[1] 陈后金[1] 李居朋[1] 姚畅[1] 杨娜[1] 

机构地区：[1]北京交通大学电子信息工程学院

出　　处：《电子学报》2011年第12期2796-2800,共5页Acta Electronica Sinica

基　　金：中央高校基本科研业务费专项资金资助(No.2009YJS003);国家自然科学基金(No.60872081);北京市自然科学基金(No.4092034)

摘　　要：多数现有的压缩感知重建算法基于单观测向量,处理图像信号时将其表示成一维信号,算法效率低,重建图像的效果也有待提高.本文提出了一种基于多观测向量和稀疏贝叶斯学习的重建算法,通过同时处理观测矩阵的每一列直接求得加权系数矩阵,从而快速重建图像.在相同的采样率条件下,该算法的重建图像效果更好,算法效率明显提高.采用标准测试图像进行实验,验证了算法的有效性.Most existing compressed sensing reconstruction algorithms are based on single measurement vector.When processing image signal,the efficiency of these algorithms is low because the image is treated as one-dimension signal and the quality of the reconstructed image needs to be improved.A reconstruction algorithm based on multiple measurement vectors and sparse Bayesian learning is proposed in this paper.The image is reconstructed quickly because the weighting coefficient matrix can be got directly by processing each column of the measurement matrix simultaneously.The proposed algorithm has better reconstructed image and the efficiency has been improved significantly under the same sample rate.The validity of the proposed algorithm is proved by the experiments to the standard test images.

关 键 词：图像重建 压缩感知 稀疏贝叶斯学习 多观测向量 

分 类 号：TN911.73[电子电信—通信与信息系统]