DLMMPR:Deep Learning-based Measurement Matrix for Phase Retrieval
This work addresses the challenge of robust phase recovery in imaging and signal processing, representing an incremental improvement through a novel hybrid method.
The paper tackles the problem of designing measurement matrices for phase retrieval by integrating learning optimization into an end-to-end deep learning architecture, resulting in substantial gains in PSNR and SSIM compared to benchmarks like DeepMMSE and PrComplex.
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.