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Proceedings Paper

Application of Shannon Information in image postprocessing
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Paper Abstract

A crucial step in image restoration involves deconvolving the true object from noisy and often poorly sampled image data. Deconvolution under these conditions represents an ill-posed inversion problem, in that no unique computationally stable solution exists. We propose a statistical information based approach to regularize the deconvolution process. Using Shannon Information, one monitors the information about the object that is processed during the deconvolution in order to obtain an optimal stopping criterion and hence the``best' solution to the inversion problem. The optimal stopping criterion is based on how Shannon Information changes in the spatial frequency domain as the deconvolution proceeds. We present results for the Maximum Entropy Method (MEM) and Richardson-Lucy (RL) non-linear deconvolution techniques.

Paper Details

Date Published: 30 July 2002
PDF: 7 pages
Proc. SPIE 4736, Visual Information Processing XI, (30 July 2002); doi: 10.1117/12.477587
Show Author Affiliations
Douglas A. Hope, Univ. of New Mexico (United States)
Sudhakar Prasad, Univ. of New Mexico (United States)


Published in SPIE Proceedings Vol. 4736:
Visual Information Processing XI
Zia-ur Rahman; Robert A. Schowengerdt; Stephen E. Reichenbach, Editor(s)

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