NDLI: p-th Power total variation regularization in photon-limited imaging via iterative reweighting

Content Provider	IEEE Xplore Digital Library
Author	Adhikari, L. Marcia, R.F.
Copyright Year	2015
Description	Author affiliation: Dept. of Appl. Math., Univ. of California, Merced, Merced, CA, USA (Adhikari, L.; Marcia, R.F.)
Abstract	Recent work in $ℓ_{p}-norm$ regularized sparsity recovery problems (where 0 ≤ p ≤ 1) has shown that signals can be recovered with very high accuracy despite the fact that the solution to these nonconvex optimization problems are not necessarily the global minima but are instead potentially local minima. In particular, $ℓ_{p}-norm$ regularization has been used effectively for signal reconstruction from measurements corrupted by zero-mean additive Gaussian noise. This paper describes a p-th power total variation $(TV_{p})$ regularized op timization approach for image recovery problems in photon-limited settings using iterative reweighting. The proposed method iteratively convexities a sequence of nonconvex $TV_{p}$ subproblems using a weighted TV approach and is solved using a modification to the FISTA method for TV-based de-noising. We explore the effectiveness of the proposed method through numerical experiments in image deblurring.
Starting Page	1621
Ending Page	1625
File Size	160664
Page Count	5
File Format	PDF
ISBN	9780992862633
ISSN	20761465
DOI	10.1109/EUSIPCO.2015.7362658
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-31
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	EURASIP
Subject Keyword	Photon-limited imaging TV SPIRAL-TVp Europe Signal processing Minimization Total variation (TV) Noise measurement Poisson noise Image reconstruction Photonics Weighted TV
Content Type	Text
Resource Type	Article

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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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