ACCURATE AND EFFICIENT IMAGE RECONSTRUCTION FROM MULTIPLE MEASUREMENTS OF FOURIER SAMPLES

作     者：T.Scarnati Anne Gelb T.Scarnati;Anne Gelb

作者机构：Air Force Research LaboratoryWPAFBOH 45433USA Dartmouth CollegeHanoverNH 03755USA 

出 版 物：《Journal of Computational Mathematics》 (计算数学（英文）)

年 卷 期：2020年第38卷第5期

页      面：797-826页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Theresa Scarnati's work is supported in part by AFOSR LRIR Anne Gelb's work is partially supported by the grants NSF-DMS Approved for public release.PA Approval 

主　　题：Multiple measurement vectors Joint sparsity Weighted`1 Edge detection Fourier data 

摘      要：Several problems in imaging acquire multiple measurement vectors(MMVs)of Fourier samples for the same underlying *** recovery techniques from MMVs aim to exploit the joint sparsity across the measurements in the sparse *** is typically accomplished by extending the use of`1 regularization of the sparse domain in the single measurement vector(SMV)case to using`2,1 regularization so that the“jointnesscan be accounted *** effective,the approach is inherently coupled and therefore computationally *** method also does not consider current approaches in the SMV case that use spatially varying weighted`1 regularization *** recently introduced variance based joint sparsity(VBJS)recovery method uses the variance across the measurements in the sparse domain to produce a weighted MMV method that is more accurate and more efficient than the standard`2,1 *** efficiency is due to the decoupling of the measurement vectors,with the increased accuracy resulting from the spatially varying *** by these results,this paper introduces a new technique to even further reduce computational cost by eliminating the requirement to first approximate the underlying image in order to construct the *** this preprocessing step moreover reduces the amount of information lost from the data,so that our method is more *** examples provided in the paper verify these benefits.