Compressed Multi-view Imaging with Joint Reconstruction

Abstract

The newly emerging sampling methodology of compressed sensing opens a door to obtain compressed data directly. How to efficiently reconstruct the original from the compressed data becomes a new challenge problem. Many reconstruction works have been proposed on mono-view images by exploring the sparsity of the original image, how ever it is a challenge to efficiently explore the correlations between different views in compressed multi-view imaging systems. With the aid of inter-view disparity information at receiver end, a joint reconstruction approach is presented for independently captured view-point images via compressed imaging. In the proposed approach, robust estimation is obtained by formulating the occurrences of outliers, usually caused by illumination change, mismatch and discontinuity in disparity estimation, as a sparse model, which can be efficiently solved by a proximal sub gradient algorithm based on l1-norm minimization. Experimental results show that the joint reconstruction of compressed multi-view images can achieve significantly better recovery quality than the independently reconstructed ones. Thus, the proposed algorithm can be appropriately applied to multi-view capturing with distributed sensors without inter-view collaboration.