Feature-Domain FISTA-Inspired Network for Compressive Sensing

The deep unfolding network (DUN) for image compressive sensing (ICS) integrates a traditional optimization algorithm with a neural network, providing clear interpretability and demonstrating exceptional performance. Nevertheless, the inherent paradigm of the DUN lies in the independent proximal mapping between iterations and the limited information flux, potentially constraining the mapping capability of the deep unfolding method. This paper introduces a Feature-Domain FISTA-Inspired Deep Unfolding Network (FDFI-DUN) for ICS. FDFI-DUN comprises a Feature-Domain Nesterov Momentum Module (FNMM), a Feature-Domain Gradient Descent Module (FGDM), and a Two-level Multiscale Proximal Mapping Module (TMPMM). Specifically, the Nesterov momentum term and gradient descent term in the FISTA are tailored to the feature domain, enhancing the information flux of the entire DUN and augmenting the feature information within and between iterations while maintaining clear interpretability. Furthermore, the TMPMM, encompassing intra-stage and inter-stage components, is designed to further augment the information flux and effectively utilize multiscale feature information for reconstructing image details. Extensive experimental results demonstrate that the proposed FDFI-DUN surpasses state-of-the-art methods in both quality and vision. Our codes are available at: https://github.com/giant-pandada/FDFI-DUN.