Group sparse optimization for inpainting of random fields on the sphere
Chao Li, Xiaojun Chen

TL;DR
This paper introduces a novel group sparse optimization model for inpainting isotropic random fields on the sphere, utilizing spherical harmonics and a hybrid norm to preserve structure, with an efficient smoothing penalty algorithm demonstrated through numerical experiments.
Contribution
It develops a new infinite-dimensional optimization framework with a convex reduction and proposes a smoothing penalty algorithm for effective inpainting of spherical random fields.
Findings
The optimization problem is equivalent to a finite-dimensional convex problem.
The smoothing penalty algorithm effectively inpaints random fields on the sphere.
Numerical experiments show promising inpainting performance on real data.
Abstract
We propose a group sparse optimization model for inpainting of a square-integrable isotropic random field on the unit sphere, where the field is represented by spherical harmonics with random complex coefficients. In the proposed optimization model, the variable is an infinite-dimensional complex vector and the objective function is a real-valued function defined by a hybrid of the norm and non-Liptchitz norm that preserves rotational invariance property and group structure of the random complex coefficients. We show that the infinite-dimensional optimization problem is equivalent to a convexly-constrained finite-dimensional optimization problem. Moreover, we propose a smoothing penalty algorithm to solve the finite-dimensional problem via unconstrained optimization problems. We provide an approximation error bound of the inpainted random field defined by a…
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Taxonomy
TopicsAdvanced Numerical Analysis Techniques · Sparse and Compressive Sensing Techniques · 3D Shape Modeling and Analysis
