  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.
