超越单一解：面向图像压缩感知的多假设深度展开网络
Beyond Single Solution: Multi-Hypothesis Deep Unfolding Network for Image Compressive Sensing
把「单一解」的深度展开网络改成多假设协同展开：AlphaNet 为每个假设预测空间变化的步长，近端映射模块同时利用假设内与假设间相关性先验联合精修多个解，可直接迁移到其他病态逆问题的展开式求解器（去噪、去模糊、超分）中。
原文依据 a well-designed AlphaNet is introduced to dynamically predict spatially varying step sizes for all hypotheses
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期深度展开网络（DUN）通过将迭代优化与深度学习架构有效结合，推动了压缩感知（CS）的发展。然而，大多数压缩感知方法主要将推理限制在单一解空间内，忽略了压缩感知问题固有的病态性——这种病态性本质上允许多个合理的候选假设存在。本文提出了一种新的多假设协同深度展开压缩感知网络（MHC-DUN），它通过在多个解空间上联合优化，显式地建模并利用多个假设。具体而言，MHC-DUN 遵循近端梯度下降算法，在这一多假设范式下联合执行梯度下降与近端映射。i）在梯度下降方面，引入精心设计的 AlphaNet，为所有假设动态预测空间变化的步长，从而实现多个解之间的协同梯度更新。ii）在近端算子方面，设计了一个精巧的多假设协同近端映射模块，同时利用假设内与假设间的相关性先验来联合精修多个解。为实现端到端训练，本文设计了一种新的复合损失函数，它在测量保真度、假设多样性与重建精度之间取得平衡，在保持重建保真度的同时鼓励对互补解的探索。实验结果表明，所提出的压缩感知方法优于现有的压缩感知网络。
英文原文摘要 1535 字符
Recent deep unfolding networks (DUNs) have advanced Compressive Sensing (CS) by effectively integrating iterative optimization with deep learning architectures. However, most CS approaches predominantly confine their inference to a single solution space, neglecting the inherent ill-posedness of CS problems that intrinsically permits multiple plausible candidate hypotheses. In this paper, a novel Multi-Hypothesis Collaborative Deep Unfolding CS Network (MHC-DUN) is proposed, which explicitly models and leverages multiple hypotheses by jointly optimizing across diverse solution spaces. Specifically, following the Proximal Gradient Descent algorithm, MHC-DUN jointly performs gradient descent and proximal mapping within this multi-hypothesis paradigm. i) For gradient descent, a well-designed AlphaNet is introduced to dynamically predict spatially varying step sizes for all hypotheses, enabling collaborative gradient updates across multiple solutions. ii) For proximal operator, a sophisticated multi-hypothesis collaborative proximal mapping module is designed, which leverages both intra-hypothesis and inter-hypothesis correlation priors to jointly refine multiple solutions. To enable end-to-end training, a novel composite loss function is designed, which balances measurement fidelity, hypothesis diversity, and reconstruction accuracy, encouraging exploration of complementary solutions while maintaining reconstruction fidelity. Experimental results reveal that the proposed CS method outperforms existing CS networks.