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- Publisher Website: 10.1002/nla.2100
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Article: A variational approach for restoring images corrupted by noisy blur kernels and additive noise
Title | A variational approach for restoring images corrupted by noisy blur kernels and additive noise |
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Authors | |
Keywords | random blurring function convex optimization proximal alternating minimization image restoration total variation |
Issue Date | 2017 |
Citation | Numerical Linear Algebra with Applications, 2017, v. 24, n. 6, article no. e2100 How to Cite? |
Abstract | Copyright © 2017 John Wiley & Sons, Ltd. In this paper, we study a deblurring algorithm for distorted images by random impulse response. We propose and develop a convex optimization model to recover the underlying image and the blurring function simultaneously. The objective function is composed of 3 terms: the data-fitting term between the observed image and the product of the estimated blurring function and the estimated image, the squared difference between the estimated blurring function and its mean, and the total variation regularization term for the estimated image. We theoretically show that under some mild conditions, the resulting objective function can be convex in which the global minimum value is unique. The numerical results confirm that the peak-to-signal-noise-ratio and structural similarity of the restored images by the proposed algorithm are the best when the proposed objective function is convex. We also present a proximal alternating minimization scheme to solve the resulting minimization problem. Numerical examples are presented to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme. |
Persistent Identifier | http://hdl.handle.net/10722/277067 |
ISSN | 2023 Impact Factor: 1.8 2023 SCImago Journal Rankings: 0.932 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Wang, Wei | - |
dc.contributor.author | Zhao, Xile | - |
dc.date.accessioned | 2019-09-18T08:35:30Z | - |
dc.date.available | 2019-09-18T08:35:30Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Numerical Linear Algebra with Applications, 2017, v. 24, n. 6, article no. e2100 | - |
dc.identifier.issn | 1070-5325 | - |
dc.identifier.uri | http://hdl.handle.net/10722/277067 | - |
dc.description.abstract | Copyright © 2017 John Wiley & Sons, Ltd. In this paper, we study a deblurring algorithm for distorted images by random impulse response. We propose and develop a convex optimization model to recover the underlying image and the blurring function simultaneously. The objective function is composed of 3 terms: the data-fitting term between the observed image and the product of the estimated blurring function and the estimated image, the squared difference between the estimated blurring function and its mean, and the total variation regularization term for the estimated image. We theoretically show that under some mild conditions, the resulting objective function can be convex in which the global minimum value is unique. The numerical results confirm that the peak-to-signal-noise-ratio and structural similarity of the restored images by the proposed algorithm are the best when the proposed objective function is convex. We also present a proximal alternating minimization scheme to solve the resulting minimization problem. Numerical examples are presented to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme. | - |
dc.language | eng | - |
dc.relation.ispartof | Numerical Linear Algebra with Applications | - |
dc.subject | random blurring function | - |
dc.subject | convex optimization | - |
dc.subject | proximal alternating minimization | - |
dc.subject | image restoration | - |
dc.subject | total variation | - |
dc.title | A variational approach for restoring images corrupted by noisy blur kernels and additive noise | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1002/nla.2100 | - |
dc.identifier.scopus | eid_2-s2.0-85017283888 | - |
dc.identifier.volume | 24 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | article no. e2100 | - |
dc.identifier.epage | article no. e2100 | - |
dc.identifier.eissn | 1099-1506 | - |
dc.identifier.isi | WOS:000417584700005 | - |
dc.identifier.issnl | 1070-5325 | - |