View's dependency and low‐rank background‐guided compressed sensing for multi‐view image joint reconstruction

Abstract

Compressed sensing (CS) multi‐camera network reconstruction has attracted much attention in the field of distributed CS networks. However, many multi‐camera network reconstructions based on CS usually recover every image separately; the view's dependency and geometrical structure among these multi‐view images could be rarely considered in this way, which will result in some unsatisfied joint reconstruction results. Here, the authors introduce to extract the multiple view geometry from multi‐view images to construct the view's dependency observation model. Based on the proposed parametric transformation observation model, they propose a novel CS joint reconstruction method of multi‐view image that guided by the spatial correlation and low‐rank background constraints. The eventual optimisation model could be relaxed to a series of convex optimisation problems, which could be efficiently solved by combining the variable splitting and alternate iteration technique. The extended experimental results indicate that they proposed method has achieved a remarkable improvement in both objective criterion and visual fidelity compared with other competitive reconstruction methods.

A Data Encryption and Fast Transmission Algorithm Based on Surveillance Video

“…Zheng et al [15] use the sparse coding to compress data. Fei et al [16] compress the multiview image fusion. Rahaman and Paul [17] use different coding methods to compress data according to its importance.…”