用深度学习设计相位恢复的测量矩阵，提升重建质量。
DLMMPR:Deep Learning-based Measurement Matrix for Phase Retrieval
- 将测量矩阵嵌入端到端网络，通过学习优化其结构。
- 在多种噪声下相比基线方法，PSNR和SSIM显著提升。
- 适合相位恢复、成像系统优化等需要高精度重建的场景。
本文首次将学习优化引入相位恢复中的测量矩阵设计。提出基于深度学习的相位恢复测量矩阵（DLMMPR）算法，将测量矩阵参数化并融入端到端深度学习架构中。结合次梯度下降与近端映射模块，增强重建鲁棒性。在多种噪声条件下进行充分实证验证，结果表明，相比DeepMMSE和PrComplex，本方法在PSNR和SSIM上均取得显著提升，充分证明其优越性。
原文摘要 · Abstract (English)
This paper pioneers the integration of learning optimization into measurement matrix design for phase retrieval. We introduce the Deep Learning-based Measurement Matrix for Phase Retrieval (DLMMPR) algorithm, which parameterizes the measurement matrix within an end-to-end deep learning architecture. Synergistically augmented with subgradient descent and proximal mapping modules for robust recovery, DLMMPR's efficacy is decisively confirmed through comprehensive empirical validation across diverse noise regimes. Benchmarked against DeepMMSE and PrComplex, our method yields substantial gains in PSNR and SSIM, underscoring its superiority.
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