Efficient image restoration using generalized non-convex total variation regularization and Chebyshev-accelerated ADMM.
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| Title: | Efficient image restoration using generalized non-convex total variation regularization and Chebyshev-accelerated ADMM. |
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| Authors: | Kumar, Narendra1 (AUTHOR), Sonkar, Munnu2 (AUTHOR), Bhatnagar, Gaurav1 (AUTHOR) goravb@iitj.ac.in |
| Source: | Computers & Mathematics with Applications. Aug2026, Vol. 216, p18-51. 34p. |
| Subjects: | Optimization algorithms, Algorithms, Image reconstruction, Constrained optimization |
| Abstract: | This paper introduces an efficient image deblurring model that integrates a generalized non-convex total variation (TV) regularization with a hyper-Laplacian gradient prior. The proposed method aims to enhance edge retention and sparsity while preserving natural image statistics. An efficient Alternating Direction Method of Multipliers (ADMM) algorithm is developed, leveraging the Chebyshev method to solve the l p minimization problem with rapid convergence. A theoretical convergence analysis of the proposed algorithm is also provided. Extensive experiments on various real test images demonstrate that the proposed method outperforms existing techniques in both qualitative and quantitative measures. It achieves Peak Signal-to-Noise Ratio (PSNR) values between 37 dB and 42.1 dB, Structural Similarity Index (SSIM) values from 0.95 to 1, and near-perfect Feature Similarity Index (FSIM) values, with notable performance improvements observed for p in the range of 0.45 to 0.7. The proposed approach excels in preserving edge details, converges in at most 30 iterations, and proves superior to the state-of-the-art methods. The implementation of the proposed algorithm can be accessed via https://github.com/nadr123/Image_Restoration. [ABSTRACT FROM AUTHOR] |
| Copyright of Computers & Mathematics with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194448681 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Efficient image restoration using generalized non-convex total variation regularization and Chebyshev-accelerated ADMM. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumar%2C+Narendra%22">Kumar, Narendra</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sonkar%2C+Munnu%22">Sonkar, Munnu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bhatnagar%2C+Gaurav%22">Bhatnagar, Gaurav</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> goravb@iitj.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computers+%26+Mathematics+with+Applications%22">Computers & Mathematics with Applications</searchLink>. Aug2026, Vol. 216, p18-51. 34p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Constrained+optimization%22">Constrained optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper introduces an efficient image deblurring model that integrates a generalized non-convex total variation (TV) regularization with a hyper-Laplacian gradient prior. The proposed method aims to enhance edge retention and sparsity while preserving natural image statistics. An efficient Alternating Direction Method of Multipliers (ADMM) algorithm is developed, leveraging the Chebyshev method to solve the l p minimization problem with rapid convergence. A theoretical convergence analysis of the proposed algorithm is also provided. Extensive experiments on various real test images demonstrate that the proposed method outperforms existing techniques in both qualitative and quantitative measures. It achieves Peak Signal-to-Noise Ratio (PSNR) values between 37 dB and 42.1 dB, Structural Similarity Index (SSIM) values from 0.95 to 1, and near-perfect Feature Similarity Index (FSIM) values, with notable performance improvements observed for p in the range of 0.45 to 0.7. The proposed approach excels in preserving edge details, converges in at most 30 iterations, and proves superior to the state-of-the-art methods. The implementation of the proposed algorithm can be accessed via https://github.com/nadr123/Image_Restoration. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computers & Mathematics with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.camwa.2026.04.034 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 18 Subjects: – SubjectFull: Optimization algorithms Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Image reconstruction Type: general – SubjectFull: Constrained optimization Type: general Titles: – TitleFull: Efficient image restoration using generalized non-convex total variation regularization and Chebyshev-accelerated ADMM. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumar, Narendra – PersonEntity: Name: NameFull: Sonkar, Munnu – PersonEntity: Name: NameFull: Bhatnagar, Gaurav IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08981221 Numbering: – Type: volume Value: 216 Titles: – TitleFull: Computers & Mathematics with Applications Type: main |
| ResultId | 1 |
