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Proceedings Paper

Fundamental limits to noise reduction in images using support: benefits from deconvolution
Author(s): Charles L. Matson; Charles C. Beckner Jr.; Kathy J. Schulze
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Paper Abstract

The usefulness of support constraints to achieve noise reduction in images is analyzed here using an algorithm-independent Cramer-Rao bound approach. Recently, it has been shown that the amount of noise reduction achievable using support as a constraint is a function of the image-domain noise correlation properties. For image-domain delta-correlated noise sources (such as Poisson and CCD read noise), applying a support constraint does not reduce noise in the absence of deconvolution due to the lack of spatial correlation. However, when deconvolution is included in the image processing algorithm, the situation changes significantly because the deconvolution operation imposes correlations in the measurement noise. Here we present results for an invertible system blurring function showing how noise reduction occurs with support and deconvolution. In particular, we show that and explain why noise reduction preferentially occurs at the edges of the support constraint.

Paper Details

Date Published: 22 October 2004
PDF: 8 pages
Proc. SPIE 5562, Image Reconstruction from Incomplete Data III, (22 October 2004); doi: 10.1117/12.555937
Show Author Affiliations
Charles L. Matson, Air Force Research Lab. (United States)
Charles C. Beckner Jr., Air Force Research Lab. (United States)
Kathy J. Schulze, KJS Consulting (United States)

Published in SPIE Proceedings Vol. 5562:
Image Reconstruction from Incomplete Data III
Philip J. Bones; Michael A. Fiddy; Rick P. Millane, Editor(s)

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