DLMMPR:Deep Learning-based Measurement Matrix for Phase Retrieval
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Abstract (arXiv)
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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References · 10
- 2016Learning to learn by gradient descent by gradient descentcited by 616
- 2017Learning Combinatorial Optimization Algorithms over Graphscited by 557
- 2016Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applicationscited by 244
- 2018Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholdscited by 83
- 2018prDeep: Robust Phase Retrieval with a Flexible Deep Networkcited by 46
- 2020Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problemscited by 38
- 2018Real-time Power System State Estimation and Forecasting via Deep Neural Networkscited by 32
- 2013Phase Retrieval from Coded Diffraction Patternscited by 26
- 2018Better Software Analytics via "DUO": Data Mining Algorithms Using/Used-by Optimizerscited by 4
- 2023PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrievalcited by 3
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