NDLI: A recursive soft-decision PSF and neural network approach to adaptive blind image regularization

Content Provider	IEEE Xplore Digital Library
Author	Yap, K.-H. Guan, L.
Copyright Year	2000
Description	Author affiliation: Sch. of Electr. & Inf. Eng., Sydney Univ., NSW, Australia (Yap, K.-H.)
Abstract	We present a new approach to adaptive blind image regularization based on a neural network and soft-decision blur identification. We formulate blind image deconvolution into a recursive scheme by projecting and optimizing a novel cost function with respect to its image and blur subspaces. The new algorithm provides a continual blur adaptation towards the best-fit parametric structure throughout the restoration. It integrates the knowledge of real-life blur structures without compromising its flexibility in restoring images degraded by other nonstandard blurs. A nested neural network, called the hierarchical cluster model is employed to provide an adaptive, perception-based restoration. On the other hand, conjugate gradient optimization is adopted to identify the blur. Experimental results show that the new approach is effective in restoring the degraded image without the prior knowledge of the blur.
Starting Page	813
Ending Page	816
File Size	355492
Page Count	4
File Format	PDF
ISBN	0780362977
ISSN	15224880
DOI	10.1109/ICIP.2000.899580
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-09-10
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Adaptive systems Image restoration Degradation Deconvolution Cost function AWGN Australia Clustering algorithms Photography
Content Type	Text
Resource Type	Article

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