An Algorithm-Independent Analysis of the Quality of Images Produced Using Multi-Frame Blind Deconvolution Algorithms--Conference Proceedings (Postprint)

Abstract

Multi-frame blind deconvolution (MFBD) algorithms can be used to generate a deblurred image of an object from a sequence of short-exposure and atmospherically-blurred images of the object by jointly estimating the common object and all the blurring functions. In this paper we present fundamental limits (Cramer-Rao lower bounds) to the quality of restored images generated with MFBD algorithms. We show that image restorations are less noisy when using a Zemike-based point spread function (PSF) parameterization than when using a pixel-based PSF parameterization. We also show that the most noise reduction tends to occur near the edges of the true object support, even when a larger support region is used as a constraint. Finally, we show that Zernike-based PSF parameterization produces higher resolution in restored images than does pixel-based PSF parameterization.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2007
Accession Number
ADA487956

Entities

People

  • Alim Haji
  • Charles Matson

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Air Force Research Laboratories
  • Algorithms
  • Delphi Method
  • Department Of Defense
  • Directed Energy Weapons
  • Governments
  • Image Restoration
  • Information Operations
  • Military Research
  • Noise Reduction
  • Space Surveillance
  • Standards

Fields of Study

  • Physics

Readers

  • Approximation Theory.
  • Image Processing and Computer Vision.