Compressively Sensed Multi-View Image Reconstruction Using Joint Optimization Modeling

Utilizing both intra and inter views correlation plays a key role to improve compressive sensing reconstruction of multi-view images. For this goal, this paper presents a joint optimization model (JOM) for compressively-sensed multi-view image reconstruction, which jointly optimizes an adaptive disparity compensated residual total variation (ARTV) and a multi-image nonlocal low-rank tensor (MNLRT). To exploit the inter-view correlation efficiently, the ARTV method adaptively forms suitable dynamic image set to help reconstruct the current one. Different from previous work, the MNLRT regularization uses tensor rather than 2D matrix to exploit nonlocal low-rank property, which keeps intrinsic geometrical structures of image patches. An efficient algorithm is further proposed to solve the joint optimization problem via Split-Bregman based technique. Extensive experimental results demonstrate our method outperforms state-of-the-arts algorithms with almost 1.5 dB gain in terms of PSNR, while obtaining dramatically improved visual quality for edge area, especially at low sampling rates.