Title

A fast algorithm for image restoration using a recurrent neural network with bound-constrained quadratic optimization

Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

Engineering and Mathematics

RAS ID

1980

Comments

Originally published as: Gendy, S., Kothapalli, G., & Bouzerdoum, A. (2001, November). A fast algorithm for image restoration using a recurrent neural network with bound-constrained quadratic optimization. In Intelligent Information Systems Conference, The Seventh Australian and New Zealand 2001 (pp. 111-115). IEEE. Original article available here

Abstract

This paper presents a fast algorithm for a recurrent neural network that can restore a degraded image with fewer iterations and shorter processing time by using bound-constrained quadratic optimization (BCQO) and a weighted mask. The BCQO technique has already been used in signal restoration, however implementation of this method in image restoration requires considerable memory and it is computationally expensive. The proposed algorithm replaces the weight matrix of the network with a much smaller mask, thus reducing the processing time and requiring much less memory space. This algorithm produces better results than those obtained by Wiener filter, and achieves image restoration with less iterations compared to a modified Hopfield neural network.

DOI

10.1109/ANZIIS.2001.974060

 
COinS
 

Link to publisher version (DOI)

10.1109/ANZIIS.2001.974060