Multiview Image Block Compressive Sensing with Joint Multiphase Decoding for Visual Sensor Network

In this article, a multiview image compression framework, which involves the use of Block-based Compressive Sensing (BCS) and Joint Multiphase Decoding (JMD), is proposed for a Visual Sensor Network (VSN). In the proposed framework, one of the sensor nodes is configured to serve as the reference node, the others as nonreference nodes. The images are encoded independently using the BCS to produce two observed measurements that are transmitted to the host workstation. In this case, the nonreference nodes always encoded the images (I_NR) at a lower subrate when compared with the images from the reference nodes (I_R). The idea is to improve the reconstruction of I_NR using I_R. After the two observed measurements are received by the host workstation, they are first decoded independently, then image registration is applied to align I_R onto the same plane of I_NR. The aligned I_R is then fused with I_NR, using wavelets to produce the projected image I_P. Subsequently, the difference between the measurements of the I_P and I_NR is calculated. The difference is then decoded and added to I_P to produce the final reconstructed I_NR. The simulation results show that the proposed framework is able to improve the quality of I_NR on average by 2dB to 3dB at lower subrates when compared with other Compressive Sensing (CS)--based multiview image compression frameworks.