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An Inpainting Model of Fractal Group Sparse Representation Combined With Residual Denoising Network.

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Title: An Inpainting Model of Fractal Group Sparse Representation Combined With Residual Denoising Network.
Authors: Li, Zun1 (AUTHOR) lizun@xxu.edu.cn, Zhao, Wei2 (AUTHOR), Elhanashi, Abdussalam (AUTHOR) abdussalam.elhanashi@ing.unipi.it
Source: Journal of Engineering (2314-4912). 3/22/2026, Vol. 2026, p1-10. 10p.
Subjects: Image reconstruction algorithms, Image denoising, Signal-to-noise ratio, Signal denoising, Image quality in imaging systems
Abstract: Aiming at the blurring artifacts in inpainting, this paper proposes an inpainting model of fractal group sparse representation combined with a residual denoising network. On the one hand, the fractal group sparse representation model can use a unified framework to describe the local smooth and nonlocal self‐similarity features of the image to complete rough inpainting, providing important basic image information for the residual denoising network. On the other hand, regarding the denoising subproblem of fractal group sparsity, the residual denoising network can provide the fractal group sparse representation with detailed information, obtain an adaptive dictionary, optimize repaired details, reduce blurring artifacts, and achieve fine restoration. Based on fractal group sparse representation, this model introduces a residual denoising network for detail optimization, improving image sharpness and reducing artifacts. The experimental results demonstrate that the proposed model outperforms existing methods, including IRJSM, IRGSR, IRCNN, and IRFDnCNN, by achieving the best PSNR and SSIM scores, with average improvements of 2.3700% and 0.4746%, respectively. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell 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.)
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  Data: An Inpainting Model of Fractal Group Sparse Representation Combined With Residual Denoising Network.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Zun%22">Li, Zun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lizun@xxu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Wei%22">Zhao, Wei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Elhanashi%2C+Abdussalam%22">Elhanashi, Abdussalam</searchLink> (AUTHOR)<i> abdussalam.elhanashi@ing.unipi.it</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+%282314-4912%29%22">Journal of Engineering (2314-4912)</searchLink>. 3/22/2026, Vol. 2026, p1-10. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Image+reconstruction+algorithms%22">Image reconstruction algorithms</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="%22Signal+denoising%22">Signal denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Image+quality+in+imaging+systems%22">Image quality in imaging systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Aiming at the blurring artifacts in inpainting, this paper proposes an inpainting model of fractal group sparse representation combined with a residual denoising network. On the one hand, the fractal group sparse representation model can use a unified framework to describe the local smooth and nonlocal self‐similarity features of the image to complete rough inpainting, providing important basic image information for the residual denoising network. On the other hand, regarding the denoising subproblem of fractal group sparsity, the residual denoising network can provide the fractal group sparse representation with detailed information, obtain an adaptive dictionary, optimize repaired details, reduce blurring artifacts, and achieve fine restoration. Based on fractal group sparse representation, this model introduces a residual denoising network for detail optimization, improving image sharpness and reducing artifacts. The experimental results demonstrate that the proposed model outperforms existing methods, including IRJSM, IRGSR, IRCNN, and IRFDnCNN, by achieving the best PSNR and SSIM scores, with average improvements of 2.3700% and 0.4746%, respectively. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell 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:
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      – Type: doi
        Value: 10.1155/je/7540384
    Languages:
      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 1
    Subjects:
      – SubjectFull: Image reconstruction algorithms
        Type: general
      – SubjectFull: Image denoising
        Type: general
      – SubjectFull: Signal-to-noise ratio
        Type: general
      – SubjectFull: Signal denoising
        Type: general
      – SubjectFull: Image quality in imaging systems
        Type: general
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      – TitleFull: An Inpainting Model of Fractal Group Sparse Representation Combined With Residual Denoising Network.
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            NameFull: Li, Zun
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            NameFull: Zhao, Wei
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            NameFull: Elhanashi, Abdussalam
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            – D: 22
              M: 03
              Text: 3/22/2026
              Type: published
              Y: 2026
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              Value: 2026
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            – TitleFull: Journal of Engineering (2314-4912)
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