Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping


Consider an unknown smooth function f:[0,1]d→R, and say we are given n noisy mod 1 samples of f, i.e., yi=(f(xi)+ηi)mod1, for xi∈[0,1]d, where ηi denotes the noise. Given the samples (xi,yi)ni=1, our goal is to recover smooth, robust estimates of the clean samples f(xi)mod1. We formulate a natural approach for solving this problem, which works with angular embeddings of the noisy mod 1 samples over the unit circle, inspired by the angular synchronization framework. This amounts to solving a smoothness regularized least-squares problem -- a quadratically constrained quadratic program (QCQP) -- where the variables are constrained to lie on the unit circle. Our approach is based on solving its relaxation, which is a trust-region sub-problem and hence solvable efficiently. We provide theoretical guarantees demonstrating its robustness to noise for adversarial, and random Gaussian and Bernoulli noise models. To the best of our knowledge, these are the first such theoretical results for this problem. We demonstrate the robustness and efficiency of our approach via extensive numerical simulations on synthetic data, along with a simple least-squares solution for the unwrapping stage, that recovers the original samples of f (up to a global shift). It is shown to perform well at high levels of noise, when taking as input the denoised modulo 1 samples.

Finally, we also consider two other approaches for denoising the modulo 1 samples that leverage tools from Riemannian optimization on manifolds, including a Burer-Monteiro approach for a semidefinite programming relaxation of our formulation. For the two-dimensional version of the problem, which has applications in radar interferometry, we are able to solve instances of real-world data with a million sample points in under 10 seconds, on a personal laptop.

Citation information

M. Cucuringu, H. Tyagi, "Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping", arXiv:1803.03669 (2018),  accepted to Journal of Machine Learning Research (JMLR)

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