Deep Unfolding Networks(DUNs) have demonstrated remarkable success in compressed sensing by integrating opti mization solvers with deep neural networks. The issue of information loss during the unfolding process has received significant attention. To address this issue, many advanced deep unfolding networks utilize memory mechanisms to augment the information transmission during iterations. However, most of these networks only use the memory module to enhance the proximal mapping process instead of adjusting the entire iteration. In this paper, we propose an LSTM-inspired proximal gradient descent mod ule called the Gates-Controlled Iterative Module (GCIM), lead ing to a Gates-Controlled Deep Unfolding Network (GCDUN) for compressed sensing. We utilize the gate units to modulate the information flow through the iteration by forgetting the redun dant information before the gradient descent, providing necessary features for the proximal mapping stage, and selecting the key information for the next stage. To reduce parameters, we propose a parameter-friendly version called Recurrent Gates-Controlled Deep Unfolding Networks (RGCDUN), which also achieves great performance but with much fewer parameters. Extensive experi ments manifest that our networks achieve excellent performance. The source codes are available at https://github.com/coder0856/GCDUN
python == 3.8
torch == 1.11.0+cu113
Our codes are built on FSOINet, ISTA-Net and OPINE-Net. We are sincerely thankful to the authors for sharing their codes.