2D sparse signal recovery via 2D orthogonal matching pursuit
2D sparse signal recovery via 2D orthogonal matching pursuit作者机构:Space Science and Engineering CenterUniversity of Wisconsin-MadisonMadisonWI 53706USA
出 版 物:《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期:2012年第55卷第4期
页 面:889-897页
核心收录:
学科分类:0711[理学-系统科学] 07[理学] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器]
基 金:supported by National Science Foundation of China(Grant Nos.61001100,61077009,60975007) Provincial Science Foundation of Shaanxi,China(Grant Nos.2010K06-15,2010JQ8019)
主 题:compressive sampling 2D sparse signal recovery algorithm orthogonal matching pursuit
摘 要:Recovery algorithms play a key role in compressive sampling (CS).Most of current CS recovery algo-rithms are originally designed for one-dimensional (1D) signal,while many practical signals are two-dimensional (2D).By utilizing 2D separable sampling,2D signal recovery problem can be converted into 1D signal recovery problem so that ordinary 1D recovery algorithms,*** matching pursuit (OMP),can be applied ***,even with 2D separable sampling,the memory usage and complexity at the decoder are still *** paper develops a novel recovery algorithm called 2D-OMP,which is an extension of *** the 2D-OMP,each atom in the dictionary is a *** each iteration,the decoder projects the sample matrix onto 2D atoms to select the best matched atom,and then renews the weights for all the already selected atoms via the least *** show that 2D-OMP is in fact equivalent to 1D-OMP,but it reduces recovery complexity and memory usage ***’s more important,by utilizing the same methodology used in this paper,one can even obtain higher dimensional OMP (say 3D-OMP,etc.) with ease.