Sparse representation based image deblurring model under random-valued impulse noise

Sparse representation based image deblurring model under random-valued impulse noise Multidim Syst Sign Process https://doi.org/10.1007/s11045-018-0587-z Sparse representation based image deblurring model under random-valued impulse noise 1 1 Myeongmin Kang · Myungjoo Kang · Miyoun Jung Received: 7 December 2017 / Revised: 16 April 2018 / Accepted: 14 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this article, we introduce a new patch-based model for restoring images simul- taneously corrupted by blur and random-valued impulse noise. The model involves a l -norm data-fidelity term, a sparse representation prior over learned dictionaries, and the total variation (TV) regularization. Unlike previous works Cai et al. (Inverse Probl Imaging 2(2):187–204, 2008), Ma et al. (SIAM J Imaging Sci 6(4):2258–2284, 2013), one-phase approach is utilized for random-valued impulse noise. As in Yuan and Ghanem (IEEE con- ference on computer vision and pattern recognition (CVPR), pp 5369–5377, 2015), the l data-fitting term plays an influential role for removing random-valued impulse noise. Moreover, the sparse representation prior enables to preserve textures and details efficiently, whereas TV regularization locally smoothes images while keeping sharp edges. To handle nonconvex and nondifferentiable terms, we adopt a variable splitting scheme, and then the penalty method and alternating minimization algorithm are employed. This results in http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multidimensional Systems and Signal Processing Springer Journals

Sparse representation based image deblurring model under random-valued impulse noise

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Engineering; Circuits and Systems; Electrical Engineering; Signal,Image and Speech Processing; Artificial Intelligence (incl. Robotics)
ISSN
0923-6082
eISSN
1573-0824
D.O.I.
10.1007/s11045-018-0587-z
Publisher site
See Article on Publisher Site

Abstract

Multidim Syst Sign Process https://doi.org/10.1007/s11045-018-0587-z Sparse representation based image deblurring model under random-valued impulse noise 1 1 Myeongmin Kang · Myungjoo Kang · Miyoun Jung Received: 7 December 2017 / Revised: 16 April 2018 / Accepted: 14 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this article, we introduce a new patch-based model for restoring images simul- taneously corrupted by blur and random-valued impulse noise. The model involves a l -norm data-fidelity term, a sparse representation prior over learned dictionaries, and the total variation (TV) regularization. Unlike previous works Cai et al. (Inverse Probl Imaging 2(2):187–204, 2008), Ma et al. (SIAM J Imaging Sci 6(4):2258–2284, 2013), one-phase approach is utilized for random-valued impulse noise. As in Yuan and Ghanem (IEEE con- ference on computer vision and pattern recognition (CVPR), pp 5369–5377, 2015), the l data-fitting term plays an influential role for removing random-valued impulse noise. Moreover, the sparse representation prior enables to preserve textures and details efficiently, whereas TV regularization locally smoothes images while keeping sharp edges. To handle nonconvex and nondifferentiable terms, we adopt a variable splitting scheme, and then the penalty method and alternating minimization algorithm are employed. This results in

Journal

Multidimensional Systems and Signal ProcessingSpringer Journals

Published: Jun 5, 2018

References

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