Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming
Abstract Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can r...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Javed, Syed Gibran [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 75(2015), 10 vom: 30. Apr., Seite 5887-5916 |
|---|---|
| Übergeordnetes Werk: |
volume:75 ; year:2015 ; number:10 ; day:30 ; month:04 ; pages:5887-5916 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-015-2554-0 |
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OLC2035022797 |
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| 520 | |a Abstract Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt & pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighborhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways. | ||
| 650 | 4 | |a Image denoising | |
| 650 | 4 | |a Genetic programming | |
| 650 | 4 | |a Noise detection | |
| 650 | 4 | |a Mixed impulse noise | |
| 650 | 4 | |a Salt & pepper noise | |
| 650 | 4 | |a Impulse burst noise | |
| 650 | 4 | |a Statistical features | |
| 650 | 4 | |a Robust outlyingness ratio | |
| 700 | 1 | |a Majid, Abdul |4 aut | |
| 700 | 1 | |a Mirza, Anwar M. |4 aut | |
| 700 | 1 | |a Khan, Asifullah |4 aut | |
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10.1007/s11042-015-2554-0 doi (DE-627)OLC2035022797 (DE-He213)s11042-015-2554-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Javed, Syed Gibran verfasserin aut Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt & pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighborhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways. Image denoising Genetic programming Noise detection Mixed impulse noise Salt & pepper noise Impulse burst noise Statistical features Robust outlyingness ratio Majid, Abdul aut Mirza, Anwar M. aut Khan, Asifullah aut Enthalten in Multimedia tools and applications Springer US, 1995 75(2015), 10 vom: 30. Apr., Seite 5887-5916 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:75 year:2015 number:10 day:30 month:04 pages:5887-5916 https://doi.org/10.1007/s11042-015-2554-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 75 2015 10 30 04 5887-5916 |
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10.1007/s11042-015-2554-0 doi (DE-627)OLC2035022797 (DE-He213)s11042-015-2554-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Javed, Syed Gibran verfasserin aut Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt & pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighborhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways. Image denoising Genetic programming Noise detection Mixed impulse noise Salt & pepper noise Impulse burst noise Statistical features Robust outlyingness ratio Majid, Abdul aut Mirza, Anwar M. aut Khan, Asifullah aut Enthalten in Multimedia tools and applications Springer US, 1995 75(2015), 10 vom: 30. Apr., Seite 5887-5916 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:75 year:2015 number:10 day:30 month:04 pages:5887-5916 https://doi.org/10.1007/s11042-015-2554-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 75 2015 10 30 04 5887-5916 |
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10.1007/s11042-015-2554-0 doi (DE-627)OLC2035022797 (DE-He213)s11042-015-2554-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Javed, Syed Gibran verfasserin aut Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt & pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighborhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways. Image denoising Genetic programming Noise detection Mixed impulse noise Salt & pepper noise Impulse burst noise Statistical features Robust outlyingness ratio Majid, Abdul aut Mirza, Anwar M. aut Khan, Asifullah aut Enthalten in Multimedia tools and applications Springer US, 1995 75(2015), 10 vom: 30. Apr., Seite 5887-5916 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:75 year:2015 number:10 day:30 month:04 pages:5887-5916 https://doi.org/10.1007/s11042-015-2554-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 75 2015 10 30 04 5887-5916 |
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10.1007/s11042-015-2554-0 doi (DE-627)OLC2035022797 (DE-He213)s11042-015-2554-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Javed, Syed Gibran verfasserin aut Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt & pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighborhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways. Image denoising Genetic programming Noise detection Mixed impulse noise Salt & pepper noise Impulse burst noise Statistical features Robust outlyingness ratio Majid, Abdul aut Mirza, Anwar M. aut Khan, Asifullah aut Enthalten in Multimedia tools and applications Springer US, 1995 75(2015), 10 vom: 30. Apr., Seite 5887-5916 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:75 year:2015 number:10 day:30 month:04 pages:5887-5916 https://doi.org/10.1007/s11042-015-2554-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 75 2015 10 30 04 5887-5916 |
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Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming |
| abstract |
Abstract Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt & pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighborhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways. © Springer Science+Business Media New York 2015 |
| abstractGer |
Abstract Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt & pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighborhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways. © Springer Science+Business Media New York 2015 |
| abstract_unstemmed |
Abstract Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt & pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighborhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways. © Springer Science+Business Media New York 2015 |
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GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 |
| container_issue |
10 |
| title_short |
Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming |
| url |
https://doi.org/10.1007/s11042-015-2554-0 |
| remote_bool |
false |
| author2 |
Majid, Abdul Mirza, Anwar M. Khan, Asifullah |
| author2Str |
Majid, Abdul Mirza, Anwar M. Khan, Asifullah |
| ppnlink |
189064145 |
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| doi_str |
10.1007/s11042-015-2554-0 |
| up_date |
2024-07-03T23:28:43.083Z |
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7.4019384 |