Compressed Multi-view Imaging with Joint Reconstruction

Changjun Fu, Xiangyang Ji, Qionghai Dai

Abstract: The newly emerging sampling methodology of compressed sensing opens a door to obtain compressed data directly. How to efficiently reconstruct the original signal 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 multiview 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.