Image Denoising Algorithm Based on Entropy and Adaptive Fractional Order Calculus Operator
In this paper, a fractional calculus operator for image denoising is constructed based on the characteristic of local entropy and the gradient feature, and an adaptive fractional calculus image denoising algorithm is proposed. First, the effects on the entropy and gradient by noise are analyzed, res...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/7955104/ |
| _version_ | 1818617959654883328 |
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| author | Jimin Yu Lijian Tan Shangbo Zhou Liping Wang Muhammad Abubakar Siddique |
| author_facet | Jimin Yu Lijian Tan Shangbo Zhou Liping Wang Muhammad Abubakar Siddique |
| author_sort | Jimin Yu |
| collection | DOAJ |
| description | In this paper, a fractional calculus operator for image denoising is constructed based on the characteristic of local entropy and the gradient feature, and an adaptive fractional calculus image denoising algorithm is proposed. First, the effects on the entropy and gradient by noise are analyzed, respectively. Second, the noise points are regarded as small probability events in an image, and the noise points, edges, texture regions, and smooth regions are divided combining with the local structure. Finally, for improving the image denoising effect, we consider employing different fractional orders to deal with different pixels and a piecewise function is constructed to make the differential order to be adaptive. The function is with respect to the local entropy and gradient on the pixel. The experimental results show that the peak signal-to-noise ratio and the entropy (ENTROPY) of the proposed adaptive fractional calculus image denoising algorithm are higher than that of the other algorithms compared in this paper. The proposed algorithm can not only preserve image edges and texture information while removing the noise, but also obtain a good visual effect. |
| first_indexed | 2024-12-16T17:13:59Z |
| format | Article |
| id | doaj.art-24986d7fc36c46efaa5277a7d1b8ab2e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:13:59Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-24986d7fc36c46efaa5277a7d1b8ab2e2022-12-21T22:23:21ZengIEEEIEEE Access2169-35362017-01-015122751228510.1109/ACCESS.2017.27185587955104Image Denoising Algorithm Based on Entropy and Adaptive Fractional Order Calculus OperatorJimin Yu0Lijian Tan1https://orcid.org/0000-0003-4301-1749Shangbo Zhou2Liping Wang3Muhammad Abubakar Siddique4College of automation, Chongqing University of Posts and Telecommunications, Chongqing, ChinaCollege of automation, Chongqing University of Posts and Telecommunications, Chongqing, ChinaKey Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, ChinaKey Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, ChinaKhwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanIn this paper, a fractional calculus operator for image denoising is constructed based on the characteristic of local entropy and the gradient feature, and an adaptive fractional calculus image denoising algorithm is proposed. First, the effects on the entropy and gradient by noise are analyzed, respectively. Second, the noise points are regarded as small probability events in an image, and the noise points, edges, texture regions, and smooth regions are divided combining with the local structure. Finally, for improving the image denoising effect, we consider employing different fractional orders to deal with different pixels and a piecewise function is constructed to make the differential order to be adaptive. The function is with respect to the local entropy and gradient on the pixel. The experimental results show that the peak signal-to-noise ratio and the entropy (ENTROPY) of the proposed adaptive fractional calculus image denoising algorithm are higher than that of the other algorithms compared in this paper. The proposed algorithm can not only preserve image edges and texture information while removing the noise, but also obtain a good visual effect.https://ieeexplore.ieee.org/document/7955104/Entropygradientadaptivefractional calculusimage denoising |
| spellingShingle | Jimin Yu Lijian Tan Shangbo Zhou Liping Wang Muhammad Abubakar Siddique Image Denoising Algorithm Based on Entropy and Adaptive Fractional Order Calculus Operator IEEE Access Entropy gradient adaptive fractional calculus image denoising |
| title | Image Denoising Algorithm Based on Entropy and Adaptive Fractional Order Calculus Operator |
| title_full | Image Denoising Algorithm Based on Entropy and Adaptive Fractional Order Calculus Operator |
| title_fullStr | Image Denoising Algorithm Based on Entropy and Adaptive Fractional Order Calculus Operator |
| title_full_unstemmed | Image Denoising Algorithm Based on Entropy and Adaptive Fractional Order Calculus Operator |
| title_short | Image Denoising Algorithm Based on Entropy and Adaptive Fractional Order Calculus Operator |
| title_sort | image denoising algorithm based on entropy and adaptive fractional order calculus operator |
| topic | Entropy gradient adaptive fractional calculus image denoising |
| url | https://ieeexplore.ieee.org/document/7955104/ |
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