NDLI: A fast algorithm for constrained GLAD estimation with application to image restoration

Content Provider	IEEE Xplore Digital Library
Author	Youshen Xia
Copyright Year	2010
Description	Author affiliation: College of Mathematics and Computer Science, Fuzhou University, China (Youshen Xia)
Abstract	In order to relax need of the optimal regularization parameter to be estimated, a cooperative recurrent neural network (CRNN) algorithm for image restoration was presented by solving a generalized least absolute deviation (GLAD) problem. This paper proposes a fast algorithm for solving a constrained l1-norm problem which contains the GLAD problem as its special case. The proposed iterative algorithm is guaranteed to converge globally to an optimal estimate under a fixed step length. Compared with the CRNN algorithm being continuous time, the proposed iterative algorithm has a fast convergence speed. Illustrative examples with application to image restoration show that the proposed iterative algorithm has a much faster convergence rate than the CRNN algorithm.
Starting Page	729
Ending Page	733
File Size	774576
Page Count	5
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5554050
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image restoration Iterative methods Estimation Signal processing algorithms Convergence Noise Recurrent neural networks image restoration GLAD estimate constrained l1-norm problem fast algorithm
Content Type	Text
Resource Type	Article

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