NDLI: Blind deconvolution using a monotonicity constraint on the PSF

Content Provider	IEEE Xplore Digital Library
Author	Hongxin Wu Liang Ji
Copyright Year	2005
Description	Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Hongxin Wu; Liang Ji)
Abstract	It is well recognized that blind deconvolution is a severely ill-posed problem and proper constraints on the image and the system point spread function (PSF) should be applied to counteract the ill-posedness. In this paper we investigate a novel PSF constraint, the monotonicity, which means the value of the PSF monotonically decreases (or does not increase) from the center of its support. We regard the monotonicity as a common property of many simplified but well accepted PSF models, such as the geometrical model of defocus, the Gaussian model and the synthetic model in astronomical imaging. The property is utilized as a PSF constraint in a novel iterative blind deconvolution algorithm RL-CLSE. Experiments on real microscopic data show that the proposed constraint can significantly improve the quality and stability of blind deconvolution.
Sponsorship	IEEE Signal Processing Soc
File Size	2020434
File Format	PDF
ISBN	0780388747
ISSN	15206149
DOI	10.1109/ICASSP.2005.1415483
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-03-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deconvolution Solid modeling Brain modeling Atmospheric modeling Additive noise Laboratories Bioinformatics Automation Image recognition Iterative algorithms
Content Type	Text
Resource Type	Article

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