  The 2-D phase unwrapping problem aims at retrieving a "phase" image from its
modulo $2\pi$ observations. Many applications, such as interferometry or
synthetic aperture radar imaging, are concerned by this problem since they
proceed by recording complex or modulated data from which a "wrapped" phase is
extracted. Although 1-D phase unwrapping is trivial, a challenge remains in
higher dimensions to overcome two common problems: noise and discontinuities in
the true phase image. In contrast to state-of-the-art techniques, this work
aims at simultaneously unwrap and denoise the phase image. We propose a robust
convex optimization approach that enforces data fidelity constraints expressed
in the corrupted phase derivative domain while promoting a sparse phase prior.
The resulting optimization problem is solved by the Chambolle-Pock primal-dual
scheme. We show that under different observation noise levels, our approach
compares favorably to those that perform the unwrapping and denoising in two
separate steps.
