Boundary-constrained interpretable image reconstruction network for deep compressive sensing

Abstract

Compressive Sensing (CS) networks have received increasing attention due to their wide applications in data compression, single pixel imaging, medical imaging, etc. However, there are several difficult problems for CS reconstruction. First, many early CS methods treat deep networks as black boxes. Second, some of the newest methods combine iterative optimizers with neural networks to make their architecture explainable, but their performances need to be further improved. Third, most CS reconstruction methods based on deep learning ignore the significance of edge information, greatly limiting the representation ability of their reconstruction models. To this end, this paper proposes a boundary-constrained CS reconstruction model, and we cast a boundary-constrained CS reconstruction problem as two subproblems, which can be alternatively optimized. The alternatively-iterative optimization process can be expanded into an Edge Guided Interpretable image compressive sensing Network (EGINet) for image reconstruction. The proposed EGINet has three modules: an edge-aware feature-extraction module, an edge-guided intermediate-variable updating module and an intermediate-variable guided image reconstruction module. To resolve the problem of information loss in the iterative process, we introduce a multiple-memory enhancement mechanism to explore feature dependency across different iteration phases of EGINet. Meanwhile, we design a parallel-cross fusion module to selectively fuse boundary features and image features. A large number of experimental results have shown that, compared with both black-box and explainable CS reconstruction methods, the proposed EGINet has better reconstruction performances in terms of SSIM and PSNR, especially on recovering image boundary information, while maintaining network interpretability.