提出新压缩感知模型与注意力机制，提升图像重建精度与速度。
Physics-guided Deep Unfolding Network for Enhanced Kronecker Compressive sensing
- 设计不对称克罗内克压缩感知模型，增强测量非相干性。
- 引入显式梯度下降机制，学习测量中的关键特征表示。
- 融合新模型与注意力模块，在多个数据集上表现领先。
深度网络在图像压缩感知任务中取得显著进展，即从压缩测量中重建高保真图像。然而，现有方法在感知阶段缺乏非相干测量，重建阶段隐含测量表示，限制了整体性能。本文回答两个问题：1）如何提升测量非相干性以降低病态性；2）如何从测量中学习信息表示。为此，提出新型非对称克罗内克压缩感知（AKCS）模型，理论上证明其相比传统克罗内克模型具有更好非相干性，且复杂度仅轻微增加。进一步揭示，展开网络相比非展开网络的优势源于充分的梯度下降过程，即显式测量表示。提出测量感知交叉注意力（MACA）机制，用于学习隐式测量表示。将AKCS与MACA集成到广泛使用的展开架构中，构建测量增强型展开网络（MEUNet）。大量实验表明，该方法在重建精度和推理速度上均达到当前最优水平。
原文摘要 · Abstract (English)
Deep networks have achieved remarkable success in image compressed sensing (CS) task, namely reconstructing a high-fidelity image from its compressed measurement. However, existing works are deficient inincoherent compressed measurement at sensing phase and implicit measurement representations at reconstruction phase, limiting the overall performance. In this work, we answer two questions: 1) how to improve the measurement incoherence for decreasing the ill-posedness; 2) how to learn informative representations from measurements. To this end, we propose a novel asymmetric Kronecker CS (AKCS) model and theoretically present its better incoherence than previous Kronecker CS with minimal complexity increase. Moreover, we reveal that the unfolding networks' superiority over non-unfolding ones result from sufficient gradient descents, called explicit measurement representations. We propose a measurement-aware cross attention (MACA) mechanism to learn implicit measurement representations. We integrate AKCS and MACA into widely-used unfolding architecture to get a measurement-enhanced unfolding network (MEUNet). Extensive experiences demonstrate that our MEUNet achieves state-of-the-art performance in reconstruction accuracy and inference speed.
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