Undersampled Phase Retrieval with Image Priors
This work addresses the phase retrieval problem for signal processing and imaging applications, offering an incremental improvement by incorporating image priors.
The paper tackled the problem of recovering complex signals from amplitude-only measurements under severe undersampling by evaluating various image priors, achieving accurate reconstruction even below the weak recovery threshold.
Phase retrieval seeks to recover a complex signal from amplitude-only measurements, a challenging nonlinear inverse problem. Current theory and algorithms often ignore signal priors. By contrast, we evaluate here a variety of image priors in the context of severe undersampling with structured random Fourier measurements. Our results show that those priors significantly improve reconstruction, allowing accurate reconstruction even below the weak recovery threshold.