A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

Abstract Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this p...
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Autor*in:	Yu Li [verfasserIn] Zifeng Yuan [verfasserIn] Kun Zheng [verfasserIn] Luheng Jia [verfasserIn] Huaqiu Guo [verfasserIn] Hongyuan Pan [verfasserIn] Jingjing Guo [verfasserIn] Lidong Huang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: IET Image Processing - Wiley, 2021, 16(2022), 12, Seite 3325-3341
Übergeordnetes Werk:	volume:16 ; year:2022 ; number:12 ; pages:3325-3341

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1049/ipr2.12567

Katalog-ID:	DOAJ085429279

Internformat


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245	1	2	\|a A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model
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520			\|a Abstract Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state‐of‐the‐art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over‐enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image.
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700	0		\|a Lidong Huang \|e verfasserin \|4 aut
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allfields	10.1049/ipr2.12567 doi (DE-627)DOAJ085429279 (DE-599)DOAJ9443008dc50240af97ecc06e2dfbd4c3 DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Yu Li verfasserin aut A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state‐of‐the‐art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over‐enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image. Photography Computer software Zifeng Yuan verfasserin aut Kun Zheng verfasserin aut Luheng Jia verfasserin aut Huaqiu Guo verfasserin aut Hongyuan Pan verfasserin aut Jingjing Guo verfasserin aut Lidong Huang verfasserin aut In IET Image Processing Wiley, 2021 16(2022), 12, Seite 3325-3341 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:16 year:2022 number:12 pages:3325-3341 https://doi.org/10.1049/ipr2.12567 kostenfrei https://doaj.org/article/9443008dc50240af97ecc06e2dfbd4c3 kostenfrei https://doi.org/10.1049/ipr2.12567 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 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_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 16 2022 12 3325-3341
spelling	10.1049/ipr2.12567 doi (DE-627)DOAJ085429279 (DE-599)DOAJ9443008dc50240af97ecc06e2dfbd4c3 DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Yu Li verfasserin aut A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state‐of‐the‐art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over‐enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image. Photography Computer software Zifeng Yuan verfasserin aut Kun Zheng verfasserin aut Luheng Jia verfasserin aut Huaqiu Guo verfasserin aut Hongyuan Pan verfasserin aut Jingjing Guo verfasserin aut Lidong Huang verfasserin aut In IET Image Processing Wiley, 2021 16(2022), 12, Seite 3325-3341 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:16 year:2022 number:12 pages:3325-3341 https://doi.org/10.1049/ipr2.12567 kostenfrei https://doaj.org/article/9443008dc50240af97ecc06e2dfbd4c3 kostenfrei https://doi.org/10.1049/ipr2.12567 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 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_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 16 2022 12 3325-3341
allfields_unstemmed	10.1049/ipr2.12567 doi (DE-627)DOAJ085429279 (DE-599)DOAJ9443008dc50240af97ecc06e2dfbd4c3 DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Yu Li verfasserin aut A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state‐of‐the‐art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over‐enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image. Photography Computer software Zifeng Yuan verfasserin aut Kun Zheng verfasserin aut Luheng Jia verfasserin aut Huaqiu Guo verfasserin aut Hongyuan Pan verfasserin aut Jingjing Guo verfasserin aut Lidong Huang verfasserin aut In IET Image Processing Wiley, 2021 16(2022), 12, Seite 3325-3341 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:16 year:2022 number:12 pages:3325-3341 https://doi.org/10.1049/ipr2.12567 kostenfrei https://doaj.org/article/9443008dc50240af97ecc06e2dfbd4c3 kostenfrei https://doi.org/10.1049/ipr2.12567 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 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_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 16 2022 12 3325-3341
allfieldsGer	10.1049/ipr2.12567 doi (DE-627)DOAJ085429279 (DE-599)DOAJ9443008dc50240af97ecc06e2dfbd4c3 DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Yu Li verfasserin aut A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state‐of‐the‐art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over‐enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image. Photography Computer software Zifeng Yuan verfasserin aut Kun Zheng verfasserin aut Luheng Jia verfasserin aut Huaqiu Guo verfasserin aut Hongyuan Pan verfasserin aut Jingjing Guo verfasserin aut Lidong Huang verfasserin aut In IET Image Processing Wiley, 2021 16(2022), 12, Seite 3325-3341 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:16 year:2022 number:12 pages:3325-3341 https://doi.org/10.1049/ipr2.12567 kostenfrei https://doaj.org/article/9443008dc50240af97ecc06e2dfbd4c3 kostenfrei https://doi.org/10.1049/ipr2.12567 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 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_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 16 2022 12 3325-3341
allfieldsSound	10.1049/ipr2.12567 doi (DE-627)DOAJ085429279 (DE-599)DOAJ9443008dc50240af97ecc06e2dfbd4c3 DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Yu Li verfasserin aut A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state‐of‐the‐art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over‐enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image. Photography Computer software Zifeng Yuan verfasserin aut Kun Zheng verfasserin aut Luheng Jia verfasserin aut Huaqiu Guo verfasserin aut Hongyuan Pan verfasserin aut Jingjing Guo verfasserin aut Lidong Huang verfasserin aut In IET Image Processing Wiley, 2021 16(2022), 12, Seite 3325-3341 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:16 year:2022 number:12 pages:3325-3341 https://doi.org/10.1049/ipr2.12567 kostenfrei https://doaj.org/article/9443008dc50240af97ecc06e2dfbd4c3 kostenfrei https://doi.org/10.1049/ipr2.12567 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 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_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 16 2022 12 3325-3341
language	English
source	In IET Image Processing 16(2022), 12, Seite 3325-3341 volume:16 year:2022 number:12 pages:3325-3341
sourceStr	In IET Image Processing 16(2022), 12, Seite 3325-3341 volume:16 year:2022 number:12 pages:3325-3341
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Photography Computer software
isfreeaccess_bool	true
container_title	IET Image Processing
authorswithroles_txt_mv	Yu Li @@aut@@ Zifeng Yuan @@aut@@ Kun Zheng @@aut@@ Luheng Jia @@aut@@ Huaqiu Guo @@aut@@ Hongyuan Pan @@aut@@ Jingjing Guo @@aut@@ Lidong Huang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	527265993
id	DOAJ085429279
language_de	englisch
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However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. 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callnumber-first	T - Technology
author	Yu Li
spellingShingle	Yu Li misc TR1-1050 misc QA76.75-76.765 misc Photography misc Computer software A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model
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topic_title	TR1-1050 QA76.75-76.765 A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model
topic	misc TR1-1050 misc QA76.75-76.765 misc Photography misc Computer software
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title	A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model
ctrlnum	(DE-627)DOAJ085429279 (DE-599)DOAJ9443008dc50240af97ecc06e2dfbd4c3
title_full	A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model
author_sort	Yu Li
journal	IET Image Processing
journalStr	IET Image Processing
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author_browse	Yu Li Zifeng Yuan Kun Zheng Luheng Jia Huaqiu Guo Hongyuan Pan Jingjing Guo Lidong Huang
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author-letter	Yu Li
doi_str_mv	10.1049/ipr2.12567
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title_sort	novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model
callnumber	TR1-1050
title_auth	A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model
abstract	Abstract Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state‐of‐the‐art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over‐enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image.
abstractGer	Abstract Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state‐of‐the‐art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over‐enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image.
abstract_unstemmed	Abstract Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state‐of‐the‐art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over‐enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image.
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title_short	A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model
url	https://doi.org/10.1049/ipr2.12567 https://doaj.org/article/9443008dc50240af97ecc06e2dfbd4c3 https://doaj.org/toc/1751-9659 https://doaj.org/toc/1751-9667
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author2	Zifeng Yuan Kun Zheng Luheng Jia Huaqiu Guo Hongyuan Pan Jingjing Guo Lidong Huang
author2Str	Zifeng Yuan Kun Zheng Luheng Jia Huaqiu Guo Hongyuan Pan Jingjing Guo Lidong Huang
ppnlink	527265993
callnumber-subject	TR - Photography
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isOA_txt	true
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doi_str	10.1049/ipr2.12567
callnumber-a	TR1-1050
up_date	2024-07-03T14:42:20.215Z
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However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. 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A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

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