Adaptive underwater image enhancement based on color compensation and fusion

Abstract Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater im...
Ausführliche Beschreibung

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Autor*in:	Zhu, Xuedong [verfasserIn] Lin, Mingxing Zhao, Mingyue Fan, Wenjing Dai, Chenggang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Underwater image enhancement Color compensation Color correction Local adaptive contrast enhancement Image fusion

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 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: Signal, image and video processing - London [u.a.] : Springer, 2007, 17(2023), 5 vom: 09. Feb., Seite 2201-2210
Übergeordnetes Werk:	volume:17 ; year:2023 ; number:5 ; day:09 ; month:02 ; pages:2201-2210

Links:	Volltext

DOI / URN:	10.1007/s11760-022-02435-5

Katalog-ID:	SPR052515192

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520			\|a Abstract Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater image, followed by color correction. And we continue to apply local adaptive contrast enhancement to the color-corrected image. Finally, we combine the contrast-enhanced image with the color-corrected image using a multi-scale fusion technique. The algorithm can more successfully increase underwater image deviation and blurring, as well as image contrast and local detail enhancement, according to experiments, which demonstrate that it is more adaptable to varied underwater scene types than existing methods.
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allfields	10.1007/s11760-022-02435-5 doi (DE-627)SPR052515192 (SPR)s11760-022-02435-5-e DE-627 ger DE-627 rakwb eng Zhu, Xuedong verfasserin aut Adaptive underwater image enhancement based on color compensation and fusion 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 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 Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater image, followed by color correction. And we continue to apply local adaptive contrast enhancement to the color-corrected image. Finally, we combine the contrast-enhanced image with the color-corrected image using a multi-scale fusion technique. The algorithm can more successfully increase underwater image deviation and blurring, as well as image contrast and local detail enhancement, according to experiments, which demonstrate that it is more adaptable to varied underwater scene types than existing methods. Underwater image enhancement (dpeaa)DE-He213 Color compensation (dpeaa)DE-He213 Color correction (dpeaa)DE-He213 Local adaptive contrast enhancement (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Lin, Mingxing aut Zhao, Mingyue aut Fan, Wenjing aut Dai, Chenggang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 17(2023), 5 vom: 09. Feb., Seite 2201-2210 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:17 year:2023 number:5 day:09 month:02 pages:2201-2210 https://dx.doi.org/10.1007/s11760-022-02435-5 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_101 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 17 2023 5 09 02 2201-2210
spelling	10.1007/s11760-022-02435-5 doi (DE-627)SPR052515192 (SPR)s11760-022-02435-5-e DE-627 ger DE-627 rakwb eng Zhu, Xuedong verfasserin aut Adaptive underwater image enhancement based on color compensation and fusion 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 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 Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater image, followed by color correction. And we continue to apply local adaptive contrast enhancement to the color-corrected image. Finally, we combine the contrast-enhanced image with the color-corrected image using a multi-scale fusion technique. The algorithm can more successfully increase underwater image deviation and blurring, as well as image contrast and local detail enhancement, according to experiments, which demonstrate that it is more adaptable to varied underwater scene types than existing methods. Underwater image enhancement (dpeaa)DE-He213 Color compensation (dpeaa)DE-He213 Color correction (dpeaa)DE-He213 Local adaptive contrast enhancement (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Lin, Mingxing aut Zhao, Mingyue aut Fan, Wenjing aut Dai, Chenggang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 17(2023), 5 vom: 09. Feb., Seite 2201-2210 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:17 year:2023 number:5 day:09 month:02 pages:2201-2210 https://dx.doi.org/10.1007/s11760-022-02435-5 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_101 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 17 2023 5 09 02 2201-2210
allfields_unstemmed	10.1007/s11760-022-02435-5 doi (DE-627)SPR052515192 (SPR)s11760-022-02435-5-e DE-627 ger DE-627 rakwb eng Zhu, Xuedong verfasserin aut Adaptive underwater image enhancement based on color compensation and fusion 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 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 Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater image, followed by color correction. And we continue to apply local adaptive contrast enhancement to the color-corrected image. Finally, we combine the contrast-enhanced image with the color-corrected image using a multi-scale fusion technique. The algorithm can more successfully increase underwater image deviation and blurring, as well as image contrast and local detail enhancement, according to experiments, which demonstrate that it is more adaptable to varied underwater scene types than existing methods. Underwater image enhancement (dpeaa)DE-He213 Color compensation (dpeaa)DE-He213 Color correction (dpeaa)DE-He213 Local adaptive contrast enhancement (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Lin, Mingxing aut Zhao, Mingyue aut Fan, Wenjing aut Dai, Chenggang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 17(2023), 5 vom: 09. Feb., Seite 2201-2210 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:17 year:2023 number:5 day:09 month:02 pages:2201-2210 https://dx.doi.org/10.1007/s11760-022-02435-5 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_101 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 17 2023 5 09 02 2201-2210
allfieldsGer	10.1007/s11760-022-02435-5 doi (DE-627)SPR052515192 (SPR)s11760-022-02435-5-e DE-627 ger DE-627 rakwb eng Zhu, Xuedong verfasserin aut Adaptive underwater image enhancement based on color compensation and fusion 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 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 Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater image, followed by color correction. And we continue to apply local adaptive contrast enhancement to the color-corrected image. Finally, we combine the contrast-enhanced image with the color-corrected image using a multi-scale fusion technique. The algorithm can more successfully increase underwater image deviation and blurring, as well as image contrast and local detail enhancement, according to experiments, which demonstrate that it is more adaptable to varied underwater scene types than existing methods. Underwater image enhancement (dpeaa)DE-He213 Color compensation (dpeaa)DE-He213 Color correction (dpeaa)DE-He213 Local adaptive contrast enhancement (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Lin, Mingxing aut Zhao, Mingyue aut Fan, Wenjing aut Dai, Chenggang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 17(2023), 5 vom: 09. Feb., Seite 2201-2210 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:17 year:2023 number:5 day:09 month:02 pages:2201-2210 https://dx.doi.org/10.1007/s11760-022-02435-5 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_101 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 17 2023 5 09 02 2201-2210
allfieldsSound	10.1007/s11760-022-02435-5 doi (DE-627)SPR052515192 (SPR)s11760-022-02435-5-e DE-627 ger DE-627 rakwb eng Zhu, Xuedong verfasserin aut Adaptive underwater image enhancement based on color compensation and fusion 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 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 Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater image, followed by color correction. And we continue to apply local adaptive contrast enhancement to the color-corrected image. Finally, we combine the contrast-enhanced image with the color-corrected image using a multi-scale fusion technique. The algorithm can more successfully increase underwater image deviation and blurring, as well as image contrast and local detail enhancement, according to experiments, which demonstrate that it is more adaptable to varied underwater scene types than existing methods. Underwater image enhancement (dpeaa)DE-He213 Color compensation (dpeaa)DE-He213 Color correction (dpeaa)DE-He213 Local adaptive contrast enhancement (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Lin, Mingxing aut Zhao, Mingyue aut Fan, Wenjing aut Dai, Chenggang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 17(2023), 5 vom: 09. Feb., Seite 2201-2210 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:17 year:2023 number:5 day:09 month:02 pages:2201-2210 https://dx.doi.org/10.1007/s11760-022-02435-5 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_101 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 17 2023 5 09 02 2201-2210
language	English
source	Enthalten in Signal, image and video processing 17(2023), 5 vom: 09. Feb., Seite 2201-2210 volume:17 year:2023 number:5 day:09 month:02 pages:2201-2210
sourceStr	Enthalten in Signal, image and video processing 17(2023), 5 vom: 09. Feb., Seite 2201-2210 volume:17 year:2023 number:5 day:09 month:02 pages:2201-2210
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Underwater image enhancement Color compensation Color correction Local adaptive contrast enhancement Image fusion
isfreeaccess_bool	false
container_title	Signal, image and video processing
authorswithroles_txt_mv	Zhu, Xuedong @@aut@@ Lin, Mingxing @@aut@@ Zhao, Mingyue @@aut@@ Fan, Wenjing @@aut@@ Dai, Chenggang @@aut@@
publishDateDaySort_date	2023-02-09T00:00:00Z
hierarchy_top_id	546899102
id	SPR052515192
language_de	englisch
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author	Zhu, Xuedong
spellingShingle	Zhu, Xuedong misc Underwater image enhancement misc Color compensation misc Color correction misc Local adaptive contrast enhancement misc Image fusion Adaptive underwater image enhancement based on color compensation and fusion
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topic_title	Adaptive underwater image enhancement based on color compensation and fusion Underwater image enhancement (dpeaa)DE-He213 Color compensation (dpeaa)DE-He213 Color correction (dpeaa)DE-He213 Local adaptive contrast enhancement (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213
topic	misc Underwater image enhancement misc Color compensation misc Color correction misc Local adaptive contrast enhancement misc Image fusion
topic_unstemmed	misc Underwater image enhancement misc Color compensation misc Color correction misc Local adaptive contrast enhancement misc Image fusion
topic_browse	misc Underwater image enhancement misc Color compensation misc Color correction misc Local adaptive contrast enhancement misc Image fusion
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title	Adaptive underwater image enhancement based on color compensation and fusion
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title_full	Adaptive underwater image enhancement based on color compensation and fusion
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author_browse	Zhu, Xuedong Lin, Mingxing Zhao, Mingyue Fan, Wenjing Dai, Chenggang
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doi_str_mv	10.1007/s11760-022-02435-5
title_sort	adaptive underwater image enhancement based on color compensation and fusion
title_auth	Adaptive underwater image enhancement based on color compensation and fusion
abstract	Abstract Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater image, followed by color correction. And we continue to apply local adaptive contrast enhancement to the color-corrected image. Finally, we combine the contrast-enhanced image with the color-corrected image using a multi-scale fusion technique. The algorithm can more successfully increase underwater image deviation and blurring, as well as image contrast and local detail enhancement, according to experiments, which demonstrate that it is more adaptable to varied underwater scene types than existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 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 Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater image, followed by color correction. And we continue to apply local adaptive contrast enhancement to the color-corrected image. Finally, we combine the contrast-enhanced image with the color-corrected image using a multi-scale fusion technique. The algorithm can more successfully increase underwater image deviation and blurring, as well as image contrast and local detail enhancement, according to experiments, which demonstrate that it is more adaptable to varied underwater scene types than existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 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 Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater image, followed by color correction. And we continue to apply local adaptive contrast enhancement to the color-corrected image. Finally, we combine the contrast-enhanced image with the color-corrected image using a multi-scale fusion technique. The algorithm can more successfully increase underwater image deviation and blurring, as well as image contrast and local detail enhancement, according to experiments, which demonstrate that it is more adaptable to varied underwater scene types than existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 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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container_issue	5
title_short	Adaptive underwater image enhancement based on color compensation and fusion
url	https://dx.doi.org/10.1007/s11760-022-02435-5
remote_bool	true
author2	Lin, Mingxing Zhao, Mingyue Fan, Wenjing Dai, Chenggang
author2Str	Lin, Mingxing Zhao, Mingyue Fan, Wenjing Dai, Chenggang
ppnlink	546899102
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doi_str	10.1007/s11760-022-02435-5
up_date	2024-07-04T03:03:52.450Z
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Adaptive underwater image enhancement based on color compensation and fusion

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