Block compressive sensing: Individual and joint reconstruction of correlated images

Compressive sensing provides simultaneous sensing and compression of data. Block compressive sensing (BCS) of images has gained a prominence in recent years due to low encoding complexity. In this paper, we propose the reconstruction algorithm for BCS framework based on iterative re-weighted l1 norm minimization. In the proposed algorithm, the desired signal sparsity is achieved using l1 norm minimization while Wiener filtering is incorporated as the smoothing operator. We also propose block based joint reconstruction algorithm for correlated images and video frames. The correlation is captured through the joint sparsity model by minimizing the objective function which promotes the common sparsity structure. The performance of proposed algorithms is tested on different stereo images and video data. Our analysis shows that the proposed individual reconstruction algorithm gives good compression performance as compared to existing schemes. Extensive analysis on correlated images demonstrates that the proposed joint reconstruction algorithm outperforms individual reconstruction algorithms.