EiCSNet: Efficient Iterative Neural Network for Compressed Sensing Reconstruction

Abstract

The rapid growth of sensing data demands compressed sensing (CS) in order to achieve high-density storage and fast data transmission. Deep neural networks (DNNs) have been under intensive development for the reconstruction of high-quality images from compressed data. However, the complicated auxiliary structures of DNN models in pursuit of better recovery performance lead to low computational efficiency and long reconstruction times. Furthermore, it is difficult for conventional neural network designs to reconstruct extra-high-frequency information at a very low sampling rate. In this work, we propose an efficient iterative neural network for CS reconstruction (EiCSNet). An efficient gradient extraction module is designed to replace the complex auxiliary structures in order to train the DNNs more efficiently. An iterative enhancement network is applied to make full use of the limited information available in CS for better iterative recovery. In addition, a frequency-aware weighted loss is further proposed for better image restoration quality. Our proposed compact model, EiCSNet2*1, improved the performance slightly and was nearly seven times faster than its counterparts, which shows that it has a highly efficient network design. Additionally, our complete model, EiCSNet6*1, achieved the best effect at this stage, where the average PSNR was improved by 0.37 dB for all testing sets and sampling rates.