Advanced image restoration through CIPFS-integrated mathematical transformations
The restoration of blurred images remains a critical challenge in computational image processing, necessitating advanced methodologies capable of reconstructing fine details while mitigating structural degradation. In this study, an innovative image restoration framework was introduced, employing Co...
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Acadlore Publishing Services Limited
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/123767/1/123767.pdf http://psasir.upm.edu.my/id/eprint/123767/ https://www.acadlore.com/article/ATAIML/2025_4_1/ataiml040105 |
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| author | Husain, Zakir Yow, Kai Siong |
| author_facet | Husain, Zakir Yow, Kai Siong |
| author_sort | Husain, Zakir |
| building | UPM Library |
| collection | Institutional Repository |
| content_provider | Universiti Putra Malaysia |
| content_source | UPM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The restoration of blurred images remains a critical challenge in computational image processing, necessitating advanced methodologies capable of reconstructing fine details while mitigating structural degradation. In this study, an innovative image restoration framework was introduced, employing Complex Interval Pythagorean Fuzzy Sets (CIPFSs) integrated with mathematically structured transformations to achieve enhanced deblurring performance. The proposed methodology initiates with the geometric correction of pixel-level distortions induced by blurring. A key innovation lies in the incorporation of CIPFS-based entropy, which is synergistically combined with local statistical energy to enable robust blur estimation and adaptive correction. Unlike traditional fuzzy logic-based approaches, CIPFS facilitates a more expressive modeling of uncertainty by leveraging complex interval-valued membership functions, thereby enabling nuanced differentiation of blur intensity across image regions. A fuzzy inference mechanism was utilized to guide the refinement process, ensuring that localized corrections are adaptively applied to degraded regions while leaving undistorted areas unaffected. To preserve edge integrity, a geometric step function was applied to reinforce structural boundaries and suppress over-smoothing artifacts. In the final restoration phase, structural consistency is enforced through normalization and regularization techniques to ensure coherence with the original image context. Experimental validations demonstrate that the proposed model delivers superior image clarity, improved edge sharpness, and reduced visual artifacts compared to state-of-the-art deblurring methods. Enhanced robustness against varying blur patterns and noise intensities was also confirmed, indicating strong generalization potential. By unifying the expressive power of CIPFS with analytically driven restoration strategies, this approach contributes a significant advancement to the domain of image deblurring and restoration under uncertainty. |
| format | Article |
| id | my.upm.eprints-123767 |
| institution | Universiti Putra Malaysia |
| language | en |
| publishDate | 2025 |
| publisher | Acadlore Publishing Services Limited |
| record_format | eprints |
| spelling | my.upm.eprints-1237672026-03-30T07:48:29Z http://psasir.upm.edu.my/id/eprint/123767/ Advanced image restoration through CIPFS-integrated mathematical transformations Husain, Zakir Yow, Kai Siong The restoration of blurred images remains a critical challenge in computational image processing, necessitating advanced methodologies capable of reconstructing fine details while mitigating structural degradation. In this study, an innovative image restoration framework was introduced, employing Complex Interval Pythagorean Fuzzy Sets (CIPFSs) integrated with mathematically structured transformations to achieve enhanced deblurring performance. The proposed methodology initiates with the geometric correction of pixel-level distortions induced by blurring. A key innovation lies in the incorporation of CIPFS-based entropy, which is synergistically combined with local statistical energy to enable robust blur estimation and adaptive correction. Unlike traditional fuzzy logic-based approaches, CIPFS facilitates a more expressive modeling of uncertainty by leveraging complex interval-valued membership functions, thereby enabling nuanced differentiation of blur intensity across image regions. A fuzzy inference mechanism was utilized to guide the refinement process, ensuring that localized corrections are adaptively applied to degraded regions while leaving undistorted areas unaffected. To preserve edge integrity, a geometric step function was applied to reinforce structural boundaries and suppress over-smoothing artifacts. In the final restoration phase, structural consistency is enforced through normalization and regularization techniques to ensure coherence with the original image context. Experimental validations demonstrate that the proposed model delivers superior image clarity, improved edge sharpness, and reduced visual artifacts compared to state-of-the-art deblurring methods. Enhanced robustness against varying blur patterns and noise intensities was also confirmed, indicating strong generalization potential. By unifying the expressive power of CIPFS with analytically driven restoration strategies, this approach contributes a significant advancement to the domain of image deblurring and restoration under uncertainty. Acadlore Publishing Services Limited 2025-03-28 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/123767/1/123767.pdf Husain, Zakir and Yow, Kai Siong (2025) Advanced image restoration through CIPFS-integrated mathematical transformations. Acadlore Transactions on AI and Machine Learning, 4 (1). pp. 50-61. ISSN 2957-9562; eISSN: 2957-9570 https://www.acadlore.com/article/ATAIML/2025_4_1/ataiml040105 Computer Science Engineering Mathematics 10.56578/ataiml040105 |
| spellingShingle | Computer Science Engineering Mathematics Husain, Zakir Yow, Kai Siong Advanced image restoration through CIPFS-integrated mathematical transformations |
| title | Advanced image restoration through CIPFS-integrated mathematical transformations |
| title_full | Advanced image restoration through CIPFS-integrated mathematical transformations |
| title_fullStr | Advanced image restoration through CIPFS-integrated mathematical transformations |
| title_full_unstemmed | Advanced image restoration through CIPFS-integrated mathematical transformations |
| title_short | Advanced image restoration through CIPFS-integrated mathematical transformations |
| title_sort | advanced image restoration through cipfs-integrated mathematical transformations |
| topic | Computer Science Engineering Mathematics |
| url | http://psasir.upm.edu.my/id/eprint/123767/1/123767.pdf http://psasir.upm.edu.my/id/eprint/123767/ https://www.acadlore.com/article/ATAIML/2025_4_1/ataiml040105 |
| url_provider | http://psasir.upm.edu.my/ |