UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing

Deep unfolding networks (DUNs), renowned for their in-terpretability and superior performance, have invigorated the realm of compressive sensing (CS). Nonetheless, existing DUNs frequently suffer from issues related to insufficient feature extraction and feature attrition during the it-erative steps. In this paper, we propose Unrolling Fixed-point Continuous Network (UFC-Net), a novel deep CS framework motivated by the traditional fixed-point contin-uous optimization algorithm. Specifically, we introduce Convolution-guided Attention Module (CAM) to serve as a critical constituent within the reconstruction phase, encompassing tailored components such as Multi-head Attention Residual Block (MARB), Auxiliary Iterative Reconstruction Block (AIRB), etc. MARB effectively integrates multi-head attention mechanisms with convolution to reinforce feature extraction, transcending the confinement of localized attributes and facilitating the apprehension of long-range correlations. Meanwhile, AIRB introduces auxiliary vari-ables, significantly bolstering the preservation of features within each iterative stage. Extensive experiments demon-strate that our proposed UFC-Net achieves remarkable per-formance both on image CS and CS-magnetic resonance imaging (CS-MRI) in contrast to state-of-the-art methods.