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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.
Guo et al. (2025) studied this question.