A novel transmission-augmented deep unfolding network with consideration of residual recovery

Abstract

Compressive sensing (CS) has been widely applied in signal processing field, especially for image reconstruction tasks. CS simplifies the sampling and compression procedures, but leaves the difficulty to the nonlinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. Nevertheless, these iterative algorithms suffer from the high computational complexity. The fashionable deep network-based methods can achieve high-precision CS reconstruction with satisfactory speed but are short of theoretical analysis and interpretability. To combine the merits of the above two kinds of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission-augmented network (SuperTA-Net) is proposed. Based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Furthermore, noting the difference between the original image and the output of SuperTA-Net, the reinforcement network is developed, where the main component called residual recovery network (RR-Net) is lightweight and can be added to reinforce all kinds of CS reconstruction networks. Experiments on reconstructing CS images demonstrate the effectiveness of the proposed networks.