Estimation of sparse signal by non-convex optimization
It is standard in compressed sensing scenarios to assume that the signal/can be sparsely represented in an orthonormal basis. Whereas, in some sense this isnt very realistic Indeed, allowing the signal to be sparse with respect to a redundant dictionary adds a lot of flexibility and significantly extends the range of applicability. In this paper, we address the problem of recover signals from undersampled data where such signals are not sparse in an orthonormal basis, but in an overcomplete dictionary. We show that if the combined matrix obeys a certain restricted isometry property and if the signal is sufficiently sparse, the reconstruction that rely on lp minimization with 0 < p < 1 is exact
compressed sensing restricted isometry property overcomplete dictionary non-convex optimization τp minimization
Wei Huang Yao Zhao Di-Rong Chen
Department of Mathematics,and LMIB Beijing University of Aeronautics and Astronautics Beijing 100083,P. R. China
国际会议
上海
英文
181-184
2011-03-11（万方平台首次上网日期，不代表论文的发表时间）