A Linear Systems Approach to Imaging Through Turbulence

  • Authors:
  • Mario Micheli;Yifei Lou;Stefano Soatto;Andrea L. Bertozzi

  • Affiliations:
  • Department of Mathematics, UCLA, Los Angeles, USA 90095-1555;School of Electrical and Computer Engineering, Georgia Inst. of Technology, Atlanta, USA 30332;Department of Computer Science, UCLA, Los Angeles, USA 90095-1596;Department of Mathematics, UCLA, Los Angeles, USA 90095-1555

  • Venue:
  • Journal of Mathematical Imaging and Vision
  • Year:
  • 2014

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper we address the problem of recovering an image from a sequence of distorted versions of it, where the distortion is caused by what is commonly referred to as ground-level turbulence. In mathematical terms, such distortion can be described as the cumulative effect of a blurring kernel and a time-dependent deformation of the image domain. We introduce a statistical dynamic model for the generation of turbulence based on linear dynamical systems (LDS). We expand the model to include the unknown image as part of the unobserved state and apply Kalman filtering to estimate such state. This operation yields a blurry image where the blurring kernel is effectively isoplanatic. Applying blind nonlocal Total Variation (NL-TV) deconvolution yields a sharp final result.