Model-Driven Enhanced Analytic Learned Iterative Shrinkage Threshold Algorithm

Abstract

The application of deep learning in compressed sensing reconstruction has achieved some excellent results. The deep neural network based on iterative algorithm can not only reflect the excellent performance of deep learning, but also reflect the interpretability of traditional compressed sensing reconstruction algorithm. The existing deep neural networks based on iterative algorithm mainly include learned iterative shrinkage threshold algorithm(LISTA), analytic learned iterative shrinkage threshold algorithm(ALISTA), etc., but each of them has its own shortcomings.We improved the network structure on the basis of predecessors, and proposed a new custom loss function to effectively improve the reconstruction performance of compressed sensing. Experiments show that our proposed neural network reduces the normalized mean square error(NMSE) by 10 dB compared with ALISTA and 15 dB compared with LISTA, and the support set accuracy of the recovered sparse signal can be optimized by our proposed custom loss function by at least 5%, especially LISTA, which has been improved by at least 80%.