电子学报2018，Vol.46Issue(3)：520-528,9.DOI:10.3969/j.issn.0372-2112.2018.03.002
采用拉普拉斯尺度混合先验的结构化近似消息传递算法
Structured Approximate Message Passing Algorithm with a Laplacian Scale Mixture Prior
摘要
Abstract
In order to reconstruct natural images from compressive sensing (CS) measurements accurately and effectively, a novel structured approximate message passing algorithm using a Laplacian scale mixture (LSM) prior is proposed. The higher-order statistical constraint of the AMP algorithm is created by the LSM model "turning the CS recovery problem into a prior information estimation problem and a singular value minimization problem. Firstly, we use the LSM distribution to model the sparsity of the singular values of the matrices built by similar patches, which denotes the similarity of image patches, and thus utilize the LSM model to describe the nonlocal similarity of images. Secondly, to obtain reliable prior information" the scale parameters of the LSM model are estimated through the use of the expectation-maximization (EM) algorithm. Finally, the singular value minimization problem is solved by the AMP algorithm to achieve the accurate image reconstruction. Experimental results show that the reconstruction quality of our structured AMP algorithm is superior to the state of art CS reconstruction algorithms.关键词
压缩感知/近似消息传递/拉普拉斯尺度混合先验/非局部相似性/期望最大化Key words
compressive sensing(CS)/approximate message passing(AMP)/Laplacian scale mixture(LSM) prior/nonlocal similarity/expectation-maximization分类
信息技术与安全科学引用本文复制引用
谢中华,马丽红..采用拉普拉斯尺度混合先验的结构化近似消息传递算法[J].电子学报,2018,46(3):520-528,9.基金项目
国家自然科学基金(No.61471173) （No.61471173）
