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Proceedings Paper

Digital image gathering and minimum mean-square error restoration
Author(s): Stephen K. Park; Stephen E. Reichenbach
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Paper 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.

Paper Details

Date Published: 1 September 1990
PDF: 12 pages
Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); doi: 10.1117/12.24172
Show Author Affiliations
Stephen K. Park, College of William and Mary (United States)
Stephen E. Reichenbach, Univ. of Nebraska (United States)


Published in SPIE Proceedings Vol. 1360:
Visual Communications and Image Processing '90: Fifth in a Series
Murat Kunt, Editor(s)

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