Random mesh projectors for inverse problems

Konik Kothari, Sidharth Gupta, Maarten V. De Hoop, Ivan Dokmanić

Research output: Contribution to conferencePaper

Abstract

We propose a new learning-based approach to solve ill-posed inverse problems in imaging. We address the case where ground truth training samples are rare and the problem is severely ill-posed-both because of the underlying physics and because we can only get few measurements. This setting is common in geophysical imaging and remote sensing. We show that in this case the common approach to directly learn the mapping from the measured data to the reconstruction becomes unstable. Instead, we propose to first learn an ensemble of simpler mappings from the data to projections of the unknown image into random piecewise-constant subspaces. We then combine the projections to form a final reconstruction by solving a deconvolution-like problem. We show experimentally that the proposed method is more robust to measurement noise and corruptions not seen during training than a directly learned inverse.

Original languageEnglish (US)
StatePublished - 2019
Event7th International Conference on Learning Representations, ICLR 2019 - New Orleans, United States
Duration: May 6 2019May 9 2019

Conference

Conference7th International Conference on Learning Representations, ICLR 2019
CountryUnited States
CityNew Orleans
Period5/6/195/9/19

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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  • Cite this

    Kothari, K., Gupta, S., De Hoop, M. V., & Dokmanić, I. (2019). Random mesh projectors for inverse problems. Paper presented at 7th International Conference on Learning Representations, ICLR 2019, New Orleans, United States.