An [formula omitted]-overlapping group sparse total variation for impulse noise image restoration.
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| Title: | An [formula omitted]-overlapping group sparse total variation for impulse noise image restoration. |
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| Authors: | Yin, Mingming1 (AUTHOR) 841034627@qq.com, Adam, Tarmizi2 (AUTHOR), Paramesran, Raveendran1 (AUTHOR) ravee58@gmail.com, Hassan, Mohd Fikree3 (AUTHOR) |
| Source: | Signal Processing: Image Communication. Mar2022, Vol. 102, pN.PAG-N.PAG. 1p. |
| Subjects: | Burst noise, Image reconstruction, Image denoising, Signal-to-noise ratio, Staircases |
| Abstract: | Total variation (TV) based methods are effective models in image restoration. For eliminating impulse noise, an effective way is to use the ℓ 1 -norm total variation model. However, the TV image restoration always yields staircase artifacts, especially in high-density noise levels. Additionally, the ℓ 1 -norm tends to over penalize solutions and is not robust to outlier characteristics of impulse noise. In this paper, we propose a new total variation model to effectively remove the staircase effects and eliminate impulse noise. The proposed model uses the ℓ 0 -norm data fidelity to effectively remove the impulse noise while the overlapping group sparse total variation (OGSTV) acts as a regularizer to eliminate the staircase artifacts. Since the proposed method requires solving an ℓ 0 -norm and an OGSTV optimization problem, a formulation using the mathematical program with equilibrium constraints (MPEC) and the majorization–minimization (MM) method are respectively used together with the alternating direction method of multipliers (ADMM). Experiments demonstrate that our proposed model performs better than several state-of-the-art algorithms such as the ℓ 1 total generalized variation, ℓ 0 total variation, and the ℓ 1 overlapping group sparse total variation in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). • The proposed model robustly eliminates impulse noise and the staircase artifacts. • The proposed model efficiently solves the non-convex model. • Significantly better results for both image denoising and deblurring. [ABSTRACT FROM AUTHOR] |
| Copyright of Signal Processing: Image Communication is the property of Elsevier B.V. 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: 154893373 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An [formula omitted]-overlapping group sparse total variation for impulse noise image restoration. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yin%2C+Mingming%22">Yin, Mingming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 841034627@qq.com</i><br /><searchLink fieldCode="AR" term="%22Adam%2C+Tarmizi%22">Adam, Tarmizi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Paramesran%2C+Raveendran%22">Paramesran, Raveendran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ravee58@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Hassan%2C+Mohd+Fikree%22">Hassan, Mohd Fikree</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Signal+Processing%3A+Image+Communication%22">Signal Processing: Image Communication</searchLink>. Mar2022, Vol. 102, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Burst+noise%22">Burst noise</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Image+denoising%22">Image denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Staircases%22">Staircases</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Total variation (TV) based methods are effective models in image restoration. For eliminating impulse noise, an effective way is to use the ℓ 1 -norm total variation model. However, the TV image restoration always yields staircase artifacts, especially in high-density noise levels. Additionally, the ℓ 1 -norm tends to over penalize solutions and is not robust to outlier characteristics of impulse noise. In this paper, we propose a new total variation model to effectively remove the staircase effects and eliminate impulse noise. The proposed model uses the ℓ 0 -norm data fidelity to effectively remove the impulse noise while the overlapping group sparse total variation (OGSTV) acts as a regularizer to eliminate the staircase artifacts. Since the proposed method requires solving an ℓ 0 -norm and an OGSTV optimization problem, a formulation using the mathematical program with equilibrium constraints (MPEC) and the majorization–minimization (MM) method are respectively used together with the alternating direction method of multipliers (ADMM). Experiments demonstrate that our proposed model performs better than several state-of-the-art algorithms such as the ℓ 1 total generalized variation, ℓ 0 total variation, and the ℓ 1 overlapping group sparse total variation in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). • The proposed model robustly eliminates impulse noise and the staircase artifacts. • The proposed model efficiently solves the non-convex model. • Significantly better results for both image denoising and deblurring. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Signal Processing: Image Communication is the property of Elsevier B.V. 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.image.2021.116620 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Burst noise Type: general – SubjectFull: Image reconstruction Type: general – SubjectFull: Image denoising Type: general – SubjectFull: Signal-to-noise ratio Type: general – SubjectFull: Staircases Type: general Titles: – TitleFull: An [formula omitted]-overlapping group sparse total variation for impulse noise image restoration. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yin, Mingming – PersonEntity: Name: NameFull: Adam, Tarmizi – PersonEntity: Name: NameFull: Paramesran, Raveendran – PersonEntity: Name: NameFull: Hassan, Mohd Fikree IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 09235965 Numbering: – Type: volume Value: 102 Titles: – TitleFull: Signal Processing: Image Communication Type: main |
| ResultId | 1 |
