Isotropic inverse-problem approach for two-dimensional phase unwrapping

TL;DR

This paper introduces a rotation-invariant inverse-problem approach for 2D phase unwrapping that minimizes an energy functional with sparsity and higher-order regularization, improving robustness and applicability.

Contribution

The paper presents a novel rotation-invariant inverse-problem method for 2D phase unwrapping using a combined sparsity and higher-order total-variation regularizer.

Findings

Effective on simulated data

Works well on real tomographic phase microscope data

Enhances applicability of quantitative phase techniques

Abstract

In this paper, we propose a new technique for two-dimensional phase unwrapping. The unwrapped phase is found as the solution of an inverse problem that consists in the minimization of an energy functional. The latter includes a weighted data-fidelity term that favors sparsity in the error between the true and wrapped phase differences, as well as a regularizer based on higher-order total-variation. One desirable feature of our method is its rotation invariance, which allows it to unwrap a much larger class of images compared to the state of the art. We demonstrate the effectiveness of our method through several experiments on simulated and real data obtained through the tomographic phase microscope. The proposed method can enhance the applicability and outreach of techniques that rely on quantitative phase evaluation.