Superior Formula for Gradient Impulse Noise Reduction from Images

Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the under...
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Autor*in:	Khudhur, Hisham M. [verfasserIn] Hassan, Basim A. Aji, Sani

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Superior formula Conjugate gradient method Convergence Impulse noise reduction Image restoration

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature India Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: International journal of applied and computational mathematics - [New Dehli] : Springer India, 2015, 10(2023), 1 vom: 08. Dez.
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:1 ; day:08 ; month:12

Links:	Volltext

DOI / URN:	10.1007/s40819-023-01637-w

Katalog-ID:	SPR054031370

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520			\|a Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the underlying image details. In the first part, we propose a new conjugate gradient parameter, and proved the global convergence using some assumptions. Then numerical experiment is conducted on some benchmark images contaminated with impulse noise to validate the efficacy of the method. The numerical results demonstrate a significant noise reduction in comparison with an existing method showing that our proposed method is more effective in denoising degraded images.
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allfields	10.1007/s40819-023-01637-w doi (DE-627)SPR054031370 (SPR)s40819-023-01637-w-e DE-627 ger DE-627 rakwb eng Khudhur, Hisham M. verfasserin aut Superior Formula for Gradient Impulse Noise Reduction from Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature India Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the underlying image details. In the first part, we propose a new conjugate gradient parameter, and proved the global convergence using some assumptions. Then numerical experiment is conducted on some benchmark images contaminated with impulse noise to validate the efficacy of the method. The numerical results demonstrate a significant noise reduction in comparison with an existing method showing that our proposed method is more effective in denoising degraded images. Superior formula (dpeaa)DE-He213 Conjugate gradient method (dpeaa)DE-He213 Convergence (dpeaa)DE-He213 Impulse noise reduction (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Hassan, Basim A. aut Aji, Sani aut Enthalten in International journal of applied and computational mathematics [New Dehli] : Springer India, 2015 10(2023), 1 vom: 08. Dez. (DE-627)815914253 (DE-600)2806624-8 2199-5796 nnns volume:10 year:2023 number:1 day:08 month:12 https://dx.doi.org/10.1007/s40819-023-01637-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 1 08 12
spelling	10.1007/s40819-023-01637-w doi (DE-627)SPR054031370 (SPR)s40819-023-01637-w-e DE-627 ger DE-627 rakwb eng Khudhur, Hisham M. verfasserin aut Superior Formula for Gradient Impulse Noise Reduction from Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature India Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the underlying image details. In the first part, we propose a new conjugate gradient parameter, and proved the global convergence using some assumptions. Then numerical experiment is conducted on some benchmark images contaminated with impulse noise to validate the efficacy of the method. The numerical results demonstrate a significant noise reduction in comparison with an existing method showing that our proposed method is more effective in denoising degraded images. Superior formula (dpeaa)DE-He213 Conjugate gradient method (dpeaa)DE-He213 Convergence (dpeaa)DE-He213 Impulse noise reduction (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Hassan, Basim A. aut Aji, Sani aut Enthalten in International journal of applied and computational mathematics [New Dehli] : Springer India, 2015 10(2023), 1 vom: 08. Dez. (DE-627)815914253 (DE-600)2806624-8 2199-5796 nnns volume:10 year:2023 number:1 day:08 month:12 https://dx.doi.org/10.1007/s40819-023-01637-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 1 08 12
allfields_unstemmed	10.1007/s40819-023-01637-w doi (DE-627)SPR054031370 (SPR)s40819-023-01637-w-e DE-627 ger DE-627 rakwb eng Khudhur, Hisham M. verfasserin aut Superior Formula for Gradient Impulse Noise Reduction from Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature India Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the underlying image details. In the first part, we propose a new conjugate gradient parameter, and proved the global convergence using some assumptions. Then numerical experiment is conducted on some benchmark images contaminated with impulse noise to validate the efficacy of the method. The numerical results demonstrate a significant noise reduction in comparison with an existing method showing that our proposed method is more effective in denoising degraded images. Superior formula (dpeaa)DE-He213 Conjugate gradient method (dpeaa)DE-He213 Convergence (dpeaa)DE-He213 Impulse noise reduction (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Hassan, Basim A. aut Aji, Sani aut Enthalten in International journal of applied and computational mathematics [New Dehli] : Springer India, 2015 10(2023), 1 vom: 08. Dez. (DE-627)815914253 (DE-600)2806624-8 2199-5796 nnns volume:10 year:2023 number:1 day:08 month:12 https://dx.doi.org/10.1007/s40819-023-01637-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 1 08 12
allfieldsGer	10.1007/s40819-023-01637-w doi (DE-627)SPR054031370 (SPR)s40819-023-01637-w-e DE-627 ger DE-627 rakwb eng Khudhur, Hisham M. verfasserin aut Superior Formula for Gradient Impulse Noise Reduction from Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature India Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the underlying image details. In the first part, we propose a new conjugate gradient parameter, and proved the global convergence using some assumptions. Then numerical experiment is conducted on some benchmark images contaminated with impulse noise to validate the efficacy of the method. The numerical results demonstrate a significant noise reduction in comparison with an existing method showing that our proposed method is more effective in denoising degraded images. Superior formula (dpeaa)DE-He213 Conjugate gradient method (dpeaa)DE-He213 Convergence (dpeaa)DE-He213 Impulse noise reduction (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Hassan, Basim A. aut Aji, Sani aut Enthalten in International journal of applied and computational mathematics [New Dehli] : Springer India, 2015 10(2023), 1 vom: 08. Dez. (DE-627)815914253 (DE-600)2806624-8 2199-5796 nnns volume:10 year:2023 number:1 day:08 month:12 https://dx.doi.org/10.1007/s40819-023-01637-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 1 08 12
allfieldsSound	10.1007/s40819-023-01637-w doi (DE-627)SPR054031370 (SPR)s40819-023-01637-w-e DE-627 ger DE-627 rakwb eng Khudhur, Hisham M. verfasserin aut Superior Formula for Gradient Impulse Noise Reduction from Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature India Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the underlying image details. In the first part, we propose a new conjugate gradient parameter, and proved the global convergence using some assumptions. Then numerical experiment is conducted on some benchmark images contaminated with impulse noise to validate the efficacy of the method. The numerical results demonstrate a significant noise reduction in comparison with an existing method showing that our proposed method is more effective in denoising degraded images. Superior formula (dpeaa)DE-He213 Conjugate gradient method (dpeaa)DE-He213 Convergence (dpeaa)DE-He213 Impulse noise reduction (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Hassan, Basim A. aut Aji, Sani aut Enthalten in International journal of applied and computational mathematics [New Dehli] : Springer India, 2015 10(2023), 1 vom: 08. Dez. (DE-627)815914253 (DE-600)2806624-8 2199-5796 nnns volume:10 year:2023 number:1 day:08 month:12 https://dx.doi.org/10.1007/s40819-023-01637-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 1 08 12
language	English
source	Enthalten in International journal of applied and computational mathematics 10(2023), 1 vom: 08. Dez. volume:10 year:2023 number:1 day:08 month:12
sourceStr	Enthalten in International journal of applied and computational mathematics 10(2023), 1 vom: 08. Dez. volume:10 year:2023 number:1 day:08 month:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Superior formula Conjugate gradient method Convergence Impulse noise reduction Image restoration
isfreeaccess_bool	false
container_title	International journal of applied and computational mathematics
authorswithroles_txt_mv	Khudhur, Hisham M. @@aut@@ Hassan, Basim A. @@aut@@ Aji, Sani @@aut@@
publishDateDaySort_date	2023-12-08T00:00:00Z
hierarchy_top_id	815914253
id	SPR054031370
language_de	englisch
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author	Khudhur, Hisham M.
spellingShingle	Khudhur, Hisham M. misc Superior formula misc Conjugate gradient method misc Convergence misc Impulse noise reduction misc Image restoration Superior Formula for Gradient Impulse Noise Reduction from Images
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topic_title	Superior Formula for Gradient Impulse Noise Reduction from Images Superior formula (dpeaa)DE-He213 Conjugate gradient method (dpeaa)DE-He213 Convergence (dpeaa)DE-He213 Impulse noise reduction (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213
topic	misc Superior formula misc Conjugate gradient method misc Convergence misc Impulse noise reduction misc Image restoration
topic_unstemmed	misc Superior formula misc Conjugate gradient method misc Convergence misc Impulse noise reduction misc Image restoration
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title	Superior Formula for Gradient Impulse Noise Reduction from Images
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title_full	Superior Formula for Gradient Impulse Noise Reduction from Images
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author_browse	Khudhur, Hisham M. Hassan, Basim A. Aji, Sani
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title_sort	superior formula for gradient impulse noise reduction from images
title_auth	Superior Formula for Gradient Impulse Noise Reduction from Images
abstract	Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the underlying image details. In the first part, we propose a new conjugate gradient parameter, and proved the global convergence using some assumptions. Then numerical experiment is conducted on some benchmark images contaminated with impulse noise to validate the efficacy of the method. The numerical results demonstrate a significant noise reduction in comparison with an existing method showing that our proposed method is more effective in denoising degraded images. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the underlying image details. In the first part, we propose a new conjugate gradient parameter, and proved the global convergence using some assumptions. Then numerical experiment is conducted on some benchmark images contaminated with impulse noise to validate the efficacy of the method. The numerical results demonstrate a significant noise reduction in comparison with an existing method showing that our proposed method is more effective in denoising degraded images. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the underlying image details. In the first part, we propose a new conjugate gradient parameter, and proved the global convergence using some assumptions. Then numerical experiment is conducted on some benchmark images contaminated with impulse noise to validate the efficacy of the method. The numerical results demonstrate a significant noise reduction in comparison with an existing method showing that our proposed method is more effective in denoising degraded images. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Superior Formula for Gradient Impulse Noise Reduction from Images
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up_date	2024-07-03T23:34:09.441Z
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this work, we present a novel approach for effectively reducing impulse noise from images using Conjugate Gradient Method. Our Proposed method used the iterative property of the conjugate gradient method to accurately identify and restore corrupted pixel values while preserving the underlying image details. In the first part, we propose a new conjugate gradient parameter, and proved the global convergence using some assumptions. Then numerical experiment is conducted on some benchmark images contaminated with impulse noise to validate the efficacy of the method. 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