NDLI: An accurate Harmony Search based algorithm for the blind deconvolution of binary images

Content Provider	IEEE Xplore Digital Library
Author	Fourie, J. Green, R. Mills, S.
Copyright Year	2010
Description	Author affiliation: Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand (Fourie, J.; Green, R.) \|\| Areograph Limited, 90 Crawford St, Dunedin, New Zealand (Mills, S.)
Abstract	In this paper we introduce the Largest Error First Harmony Search (LEFHS) algorithm for the deconvolution, or deblurring, of binary images. The algorithm requires no prior information about the source of the blur which is commonly modelled as a point spread function (PSF). LEFHS is able to fully recover the original binary image even when the PSF is unknown making it a true blind deconvolution algorithm. LEFHS addresses a major limitation that is present in another blind deconvolution algorithm called CHS. CHS is also able to fully recover the original binary image but only when the blur source is restricted to a binary PSF. LEFHS does not have this restriction and successfully recovers images blurred by arbitrary PSFs. LEFHS addresses a significant restriction in CHS as non-binary PSFs are the norm in real images. We compare our results with CHS and other state of the art binary deconvolution algorithms and find that LEFHS is faster than CHS even when using more complex non-binary PSFs as well as being more accurate than other binary deconvolution algorithms.
Starting Page	1
Ending Page	8
File Size	420676
Page Count	8
File Format	PDF
ISBN	9781424496297
ISSN	21512205
e-ISBN	9781424496310
DOI	10.1109/IVCNZ.2010.6148825
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-08
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deconvolution TV binary image recovery Noise Diversity reception harmony search Vectors Image restoration Convergence blind deconvolution
Content Type	Text
Resource Type	Article

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