Denoised Wigner Distribution Deconvolution Via Low-rank Matrix Completion
Justin Lee, Massachusetts Institute of Technology
Wigner Distribution Deconvolution (WDD) is a decades-old method for recovering phase from intensity measurements. Although the technique offers an elegant linear solution to the quadratic phase retrieval problem, it has seen limited adoption due to its high computational and memory requirements and the fact that the technique often exhibits high noise sensitivity. Here we propose a method for noise suppression in WDD. Our technique exploits the redundancy of objects phase space/mutual intensity to denoise (and complete if necessary) the WDD reconstruction. We show in model calculations that our technique outperforms other WDD algorithms as well as conventional iterative methods for phase retrieval such as ptychography.
Abstract Author(s): Justin Lee, George Barbastathis