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

Mihai Cucuringu 1 Hemant Tyagi 2 
2 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
Abstract : Consider an unknown smooth function $f : [0, 1]^d → R$, and assume we are given $n$ noisy mod 1 samples of $f , i.e., y_i = (f (x_i) + η_i) $mod 1, for $x_i \in [0, 1]^d$ , where $η_i$ denotes the noise. Given the samples $(x_i , y_i)_{i=1}^{n}$ , our goal is to recover smooth, robust estimates of the clean samples $f (x_i) mod 1$. 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 proposed 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, as well as 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 proposed approach via extensive numerical simulations on synthetic data, along with a simple least-squares based 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 synthetic aperture radar interferometry (InSAR), we are able to solve instances of real-world data with a million sample points in under 10 seconds, on a personal laptop.
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Submitted on : Monday, November 25, 2019 - 5:25:33 PM
Last modification on : Thursday, September 1, 2022 - 11:07:13 AM
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Mihai Cucuringu, Hemant Tyagi. Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping. Journal of Machine Learning Research, Microtome Publishing, 2020, 21 (32), pp.1−77. ⟨hal-02379573⟩



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