Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution
Signal Processing
2020-04-22 v3 Information Theory
math.IT
Optimization and Control
Abstract
Convex optimization recently emerges as a compelling framework for performing super resolution, garnering significant attention from multiple communities spanning signal processing, applied mathematics, and optimization. This article offers a friendly exposition to atomic norm minimization as a canonical convex approach to solve super resolution problems. The mathematical foundations and performances guarantees of this approach are presented, and its application in super resolution image reconstruction for single-molecule fluorescence microscopy are highlighted.
Cite
@article{arxiv.1904.04283,
title = {Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution},
author = {Yuejie Chi and Maxime Ferreira Da Costa},
journal= {arXiv preprint arXiv:1904.04283},
year = {2020}
}