USB-Net: Unfolding Split Bregman Method With Multi-Phase Feature Integration for Compressive Imaging
Zhen Guo, Hongping Gan
Abstract
Existing unfolding-based compressive imaging approaches always suffer from certain issues, including inefficient feature extraction and information loss during iterative reconstruction phases, which become particularly evident at low sampling ratios, i.e., significant detail degradation and distortion in reconstructed images. To mitigate these challenges, we propose USB-Net, a deep unfolding method inspired by the renowned Split Bregman algorithm and multi-phase feature integration strategy, for compressive imaging reconstruction. Specifically, we use a customized Depthwise Attention Block as a fundamental block for feature extraction, but also to address the sparse induction-related splitting operator within Split Bregman method. Based on this, we introduce three Auxiliary Iteration Modules: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {X}}^{(k)}$ </tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {D}}^{(k)}$ </tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {B}}^{(k)}$ </tex-math></inline-formula> to reinforce the effectiveness of Split Bregman’s decomposition strategy for problem breakdown and Bregman iterations. Moreover, we introduce two categories of Iterative Fusion Modules to seamlessly harmonize and integrate insights across iterative reconstruction phases, enhancing the utilization of crucial features, such as edge information and textures. In general, USB-Net can fully harness the advantages of traditional Split Bregman approach, manipulating multi-phase iterative insights to enhance feature extraction, optimize data fidelity, and achieve high-quality image reconstruction. Extensive experiments show that USB-Net significantly outperforms current state-of-the-art methods on image compressive sensing, CS-magnetic resonance imaging, and snapshot compressive imaging tasks, demonstrating superior generalizability. Our code is available at USB-Net.