Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging
Fangshu Yang

Meeting • 22 June 2020

Abstract
Phase unwrapping plays an important role for quantitative phase imaging. With the biological specimens such as organoids becoming more complex, the corresponding problem of unwrapping has become more challenging. Recently, deep-learning-based frameworks have achieved the unprecedented performance in a variety of applications; unfortunately, the end-to-end supervised-learning approaches need large representative training sets which are difficult to acquired for complex biological samples. In this talk, we present a robust and versatile framework inspired by the concept of deep image prior (DIP) for 2D phase unwrapping (PUDIP). We experimentally demonstrate the proposed method is able to faithfully unwrap the phase images on both real and simulated data without ground-truth.