NDLI: A unified minimization framework for single/multi-shot nonparametric blind deblurring

Content Provider	IEEE Xplore Digital Library
Author	Wen-Ze Shao Qi Ge Shi-Peng Xie Hai-Bo Li Hai-Song Deng Zhi-Hui Wei
Copyright Year	2015
Description	Author affiliation: Coll. of Telecommun. & Inf. Eng., Nanjing Univ. of Posts & Telecommun., Nanjing, China (Wen-Ze Shao; Qi Ge; Shi-Peng Xie; Hai-Bo Li) \|\| Sch. of Math. & Stat., Nanjing Audit Univ., Nanjing, China (Hai-Song Deng) \|\| Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China (Zhi-Hui Wei)
Abstract	The nonparametric blind deconvolution, either single or multi-shot, has been intensively studied since Fergus et al.'s variational Bayesian approach to camera shake deblurring [1]. However, in the current literature there is always a gap between the two highly related problems; single and multi-shot blind deconvolution are modeled and solved independently, lacking a unified optimization perspective. We attempt to bridge the gap between the two problems in this paper, via constructing a simple, rigorous and unified minimization functional which couples the variational Bayesian and maximum-a-posteriori principles. The new approach is depicted using a directed graphic model, in which the sharp image and the inverse noise variance associated with each shot are treated as random variables, while the blur-kernel, in difference from existing variational Bayesian methods, is just modeled as a deterministic parameter. With a universal, three-level hierarchical prior on the latent sharp image and a Gamma hyper-prior on each inverse noise variance, the single/multi-shot blind deconvolution is formulated into an ℓ0.5-norm regularized negative log-marginal-likelihood minimization problem. By ideas of expectation-maximization, majorization-minimization, mean field approximation, and iteratively reweighted least squares, all the unknown quantities of interest, including the sharp image, the blur-kernel, the inverse noise variance, as well as other related parameters are estimated automatically. In comparison to existing single/multi-shot methods, the proposed method is not only more flexible, but also more adaptive while with less implementational heuristics. Experimental results on Levin et al.'s [2] benchmark data set demonstrate the effectiveness and superiority of the proposed framework.
Starting Page	1839
Ending Page	1846
File Size	1317754
Page Count	8
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7382227
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphics Deconvolution Convolution variational Bayesian multi-shot majorization-minimization camera shake removal Minimization Bayes methods Random variables Kernel blind deblurring
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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