On denoising modulo 1 samples of a function

摘要

Consider an unknown smooth function $f: [0,1] →mathbb{R}$, and say we are given $n$ noisy $mod 1$ samples of $f$, i.e., $y_i = (f(x_i) + eta_i)mod 1$ for $x_i ∈[0,1]$, where $eta_i$ denotes 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 representations of mod 1 values over the unit circle. This amounts to solving a quadratically constrained quadratic program (QCQP) with non-convex constraints involving points lying on the unit circle. Our proposed approach is based on solving its relaxation which is a trust region subproblem, and hence solvable efficiently. We demonstrate its robustness to noise via extensive simulations on several synthetic examples, and provide a detailed theoretical analysis.