Share Email Print

Journal of Electronic Imaging

Model-based neural evaluation and iterative gradient optimization in image restoration and statistical filtering
Author(s): Ling Guan
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

An optimal model-based neural evaluation algorithm and an iterative gradient optimization algorithm used in image restoration and statistical filtering are presented. The relationship between the two algorithms is studied. We show that under the symmetric positive-definite condition, a condition easily satisfied in restoration and filtering, intra-pixel sequential processing (IPSP) of model-based neuron evaluation is equivalent to the iterative gradient optimization algorithm. We also show that although both methods provide feasible solutions to fast spatial domain implementation of restoration and filtering techniques, the iterative gradient algorithm is in fact more efficient than the IPSP neuron evaluation method. Visual examples are provided to compare the performance of the two approaches.

Paper Details

Date Published: 1 October 1995
PDF: 6 pages
J. Electron. Imag. 4(4) doi: 10.1117/12.217268
Published in: Journal of Electronic Imaging Volume 4, Issue 4
Show Author Affiliations
Ling Guan, Univ. of Sydney (Australia)

© SPIE. Terms of Use
Back to Top