Digital image gathering and minimum mean-square error restoration

Stephen K. Park, Stephen E. Reichenbach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Most digital image restoration algorithms are inherently incomplete because they are conditioned on a discrete-input, discrete-output model which only accounts for blurring during image gathering and additive noise. For those restoration applications where sampling and reconstruction (display) are important the restoration algorithm should be based on a more comprehensive end-to-end model which also accounts for the potentially important noise-like effects of aliasing and the low-pass filtering effects of interpolative reconstruction. In this paper we demonstrate that, although the mathematics of this more comprehensive model is more complex, the increase in complexity is not so great as to prevent a complete development and analysis of the associated minimum mean-square error (Wiener) restoration filter. We also survey recent results related to the important issue of implementing this restoration filter, in the spatial domain, as a computationally efficient small convolution kernel.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsMurat Kunt
PublisherPubl by Int Soc for Optical Engineering
Pages1578-1589
Number of pages12
ISBN (Print)0819404217
StatePublished - 1990
EventVisual Communications and Image Processing '90 - Lausanne, Switz
Duration: Oct 1 1990Oct 4 1990

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume1360 pt 3
ISSN (Print)0277-786X

Other

OtherVisual Communications and Image Processing '90
CityLausanne, Switz
Period10/1/9010/4/90

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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