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Proceedings Paper

Nonlinear regression for image enhancement via generalized deterministic annealing
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Paper Abstract

We introduce new classes of image enhancement techniques that are based on optimizing local characteristics of the image. Using a new optimization technique for nonconvex combinatorial optimization problems, generalized deterministic annealing (GDA), we compute fuzzy nonlinear regressions of noisy images with respect to characteristic image sets defined by certain local image models. The image enhancement results demonstrate the powerful approach of nonlinear regression and the low-cost, high-quality optimization of GDA.

Paper Details

Date Published: 22 October 1993
PDF: 12 pages
Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); doi: 10.1117/12.157948
Show Author Affiliations
Scott Thomas Acton, Univ. of Texas/Austin (United States)
Alan Conrad Bovik, Univ. of Texas/Austin (United States)

Published in SPIE Proceedings Vol. 2094:
Visual Communications and Image Processing '93
Barry G. Haskell; Hsueh-Ming Hang, Editor(s)

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