Harnessing Sparsity Over the Continuum: Atomic norm minimization for superresolution

Yuejie Chi and Maxime Ferreira Da Costa

IEEE Signal Processing Magazine, vol. 37, no. 2, pp. 39–57, 2020.

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{Chi2020Harnessing,
  author  = {Chi, Yuejie and Ferreira Da Costa, Maxime},
  title   = {Harnessing Sparsity Over the Continuum: Atomic norm minimization for superresolution},
  journal = {IEEE Signal Processing Magazine},
  volume  = {37},
  number  = {2},
  pages   = {39-57},
  year    = {2020},
  doi     = {10.1109/MSP.2019.2962209}
}