Noise Reduction in Support-Constrained Multi-Frame Blind-Deconvolution Restorations as a Function of the Number of Data Frames and the Support Constraint Sizes (Preprint)

Abstract

Multi-frame blind deconvolution (MFBD) algorithms seek to estimate jointly an object being imaged along with all the system point spread functions (PSFs) present in the measured data frames. It is well known that the quality of an object restoration improves as the number of data frames included in the restoration process is increased and as the sizes of the support constraints used in the algorithm decrease in size (while still including the true support). This improvement is due to both a greater likelihood of finding the global minimum of the MFBD cost function (when a cost-function based approach is used, of course) and the decreased noise levels in the restored image. In this paper we report on results we have obtained while investigating the latter source of improvement. We show that the amount of relative noise reduction in multi-frame blind deconvolution image restorations is greatest for just a few data frames and is a more complicated function of the support constraint sizes.

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Document Details

Document Type
Technical Report
Publication Date
Dec 06, 2006
Accession Number
ADA471576

Entities

People

  • Alim Haji
  • Charles Matson

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Air Force Facilities
  • Air Force Research Laboratories
  • Algorithms
  • Department Of Defense
  • Directed Energy Weapons
  • Image Reconstruction
  • Image Restoration
  • Imaging Techniques
  • Information Operations
  • Military Research
  • Noise
  • Noise Reduction
  • Probability Density Functions
  • Random Variables
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.