Reconstruction of multi-view compressed imaging using weighted total variation

Abstract

This paper concentrates on the problem of image reconstruction from compressed sensing (CS) measurements in multi-view compressed imaging systems, where each view is acquired independently by CS technique. In order to take advantage of both the inter-view correlation and the spatial prior information in multi-view image sets, a weighted total variation (TV) regularized model, which combines the TV norm of a target view and the TV norm of the corresponding residual, is proposed. To efficiently solve the weighted TV regularization constrained problem, novel algorithms are presented for both the anisotropy TV and the isotropy TV cases. Given the multi-view CS measurements, a sliding window-based recovery framework is also developed to work with the weighted TV-based reconstruction algorithms and produce high-quality results. We show by experiments that the proposed methods greatly outperform the straight forward reconstruction which applies view by view image reconstruction independently, and also have significant advantages over other benchmark methods.