A joint reconstruction algorithm for multi-view compressed imaging

As compressed sensing can capture signal at sub-Nyquist rate, it is suitable to apply multi-view compressed imaging framework in vision sensor networks. The image views in such networks are correlated with each other, and therefore the performance of independent view reconstruction can be further improved by joint reconstruction. In this paper, we propose a joint reconstruction algorithm, where disparity estimation and disparity compensation are used to exploit the correlation between views. The target optimization problem is divided into two sub-problems and they are solved alternately by proximal-gradient method. We show by experiments that, for a given sub-rate, the proposed joint reconstruction scheme outperforms the independent reconstruction in terms of image quality.