Robust Phase Retrieval via Reverse Kullback-Leibler Divergence

Abstract

Robustness to noise and outliers is a desirable trait in phase retrieval algorithms for many applications in imaging and signal processing. In this paper, we develop novel robust phase retrieval algorithms based on the minimization of reverse Kullback-Leibler divergence (RKLD) within the Wirtinger Flow (WF) framework. We use RKLD over intensity-only measurements in two distinct ways: i) to design a novel initial estimate based on minimum distortion design of spectral estimates, and ii) as a loss function for iterative refinement based on WF. The RKLD-based loss function offers implicit regularization by processing data at the logarithmic scale and provides the following benefits: suppressing the influence of outliers and promoting projections orthogonal to noise subspace. We perform a quantitative analysis demonstrating the robustness of RKLD-based minimization as compared to that of the $\ell_2$ and Poisson loss-based minimization. We present three algorithms based on RKLD minimization, including two with truncation schemes to enhance the robustness to significant contamination. Our numerical study uses data generated based on synthetic coded diffraction patterns and real optical imaging data. The results demonstrate the advantages of our algorithms in terms of sample efficiency, convergence speed, and robustness with respect to outliers over the state-of-the-art techniques.