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Proceedings Paper

Image restoration and diffusion processes
Author(s): Alfred S. Carasso
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Paper Abstract

A new supplementary a-priori constraint, the slow evolution from the boundary constraint, (SEB), sharply reduces noise contamination in a large class of space-invariant image deblurring problems that occur in medical, industrial, surveillance, environmental, and astronomical application. The noise suppressing properties of SEB restoration can be proved mathematically, on the basis of rigorous error bounds for the reconstruction, as a function of the noise level in the blurred image data. This analysis proceeds by reformulating the image deblurring problem into an equivalent ill-posed problem for a time-reversed diffusion equation. The SEB constraint does not require smoothness of the image. An effective, fast, non-iterative procedure, based on FFT algorithms, may be used to compute SEB restorations. For a 512 X 512 image, the procedure requires about 45 seconds of cpu time on a Sun/sparc2. A documented deblurring experiment, on an image with significant high frequency content, illustrates the computational significance of the SEB constraint by comparing SEB and Tikhonov-Miller reconstructions using optimal values of the regularization parameters.

Paper Details

Date Published: 23 June 1993
PDF: 12 pages
Proc. SPIE 2035, Mathematical Methods in Medical Imaging II, (23 June 1993); doi: 10.1117/12.146607
Show Author Affiliations
Alfred S. Carasso, National Institute of Standards and Technology (United States)

Published in SPIE Proceedings Vol. 2035:
Mathematical Methods in Medical Imaging II
Joseph N. Wilson; David C. Wilson, Editor(s)

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