Deep retinex decomposition network for underwater image enhancement
This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xu, Shuai [verfasserIn] Zhang, Jian [verfasserIn] Qin, Xin [verfasserIn] Xiao, Yuchen [verfasserIn] Qian, Jianjun [verfasserIn] Bo, Liling [verfasserIn] Zhang, Heng [verfasserIn] Li, Hongran [verfasserIn] Zhong, Zhaoman [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computers & electrical engineering - Amsterdam [u.a.] : Elsevier Science, 1973, 100 |
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| Übergeordnetes Werk: |
volume:100 |
| DOI / URN: |
10.1016/j.compeleceng.2022.107822 |
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ELV00798801X |
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| 245 | 1 | 0 | |a Deep retinex decomposition network for underwater image enhancement |
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| 520 | |a This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general idea of processing low light enhancement based on retinex, we perform color balance and illumination correction on the decomposed reflectance and illumination respectively. Finally, the fused reflectance image and illumination image are produced by post-processing to get over blurring, etc. The experiments confirm that the proposed method can retain more details and edge information. Meanwhile, compared with other underwater image enhancement methods, the proposed method performs better in terms of visual effects with nearly 20% improvement in objective evaluation. | ||
| 650 | 4 | |a Underwater image enhancement | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Image processing | |
| 650 | 4 | |a Computer vision | |
| 700 | 1 | |a Zhang, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Yuchen |e verfasserin |4 aut | |
| 700 | 1 | |a Qian, Jianjun |e verfasserin |4 aut | |
| 700 | 1 | |a Bo, Liling |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Heng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Hongran |e verfasserin |4 aut | |
| 700 | 1 | |a Zhong, Zhaoman |e verfasserin |4 aut | |
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10.1016/j.compeleceng.2022.107822 doi (DE-627)ELV00798801X (ELSEVIER)S0045-7906(22)00119-7 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Xu, Shuai verfasserin aut Deep retinex decomposition network for underwater image enhancement 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general idea of processing low light enhancement based on retinex, we perform color balance and illumination correction on the decomposed reflectance and illumination respectively. Finally, the fused reflectance image and illumination image are produced by post-processing to get over blurring, etc. The experiments confirm that the proposed method can retain more details and edge information. Meanwhile, compared with other underwater image enhancement methods, the proposed method performs better in terms of visual effects with nearly 20% improvement in objective evaluation. Underwater image enhancement Deep learning Image processing Computer vision Zhang, Jian verfasserin aut Qin, Xin verfasserin aut Xiao, Yuchen verfasserin aut Qian, Jianjun verfasserin aut Bo, Liling verfasserin aut Zhang, Heng verfasserin aut Li, Hongran verfasserin aut Zhong, Zhaoman verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 100 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 100 |
| spelling |
10.1016/j.compeleceng.2022.107822 doi (DE-627)ELV00798801X (ELSEVIER)S0045-7906(22)00119-7 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Xu, Shuai verfasserin aut Deep retinex decomposition network for underwater image enhancement 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general idea of processing low light enhancement based on retinex, we perform color balance and illumination correction on the decomposed reflectance and illumination respectively. Finally, the fused reflectance image and illumination image are produced by post-processing to get over blurring, etc. The experiments confirm that the proposed method can retain more details and edge information. Meanwhile, compared with other underwater image enhancement methods, the proposed method performs better in terms of visual effects with nearly 20% improvement in objective evaluation. Underwater image enhancement Deep learning Image processing Computer vision Zhang, Jian verfasserin aut Qin, Xin verfasserin aut Xiao, Yuchen verfasserin aut Qian, Jianjun verfasserin aut Bo, Liling verfasserin aut Zhang, Heng verfasserin aut Li, Hongran verfasserin aut Zhong, Zhaoman verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 100 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 100 |
| allfields_unstemmed |
10.1016/j.compeleceng.2022.107822 doi (DE-627)ELV00798801X (ELSEVIER)S0045-7906(22)00119-7 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Xu, Shuai verfasserin aut Deep retinex decomposition network for underwater image enhancement 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general idea of processing low light enhancement based on retinex, we perform color balance and illumination correction on the decomposed reflectance and illumination respectively. Finally, the fused reflectance image and illumination image are produced by post-processing to get over blurring, etc. The experiments confirm that the proposed method can retain more details and edge information. Meanwhile, compared with other underwater image enhancement methods, the proposed method performs better in terms of visual effects with nearly 20% improvement in objective evaluation. Underwater image enhancement Deep learning Image processing Computer vision Zhang, Jian verfasserin aut Qin, Xin verfasserin aut Xiao, Yuchen verfasserin aut Qian, Jianjun verfasserin aut Bo, Liling verfasserin aut Zhang, Heng verfasserin aut Li, Hongran verfasserin aut Zhong, Zhaoman verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 100 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 100 |
| allfieldsGer |
10.1016/j.compeleceng.2022.107822 doi (DE-627)ELV00798801X (ELSEVIER)S0045-7906(22)00119-7 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Xu, Shuai verfasserin aut Deep retinex decomposition network for underwater image enhancement 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general idea of processing low light enhancement based on retinex, we perform color balance and illumination correction on the decomposed reflectance and illumination respectively. Finally, the fused reflectance image and illumination image are produced by post-processing to get over blurring, etc. The experiments confirm that the proposed method can retain more details and edge information. Meanwhile, compared with other underwater image enhancement methods, the proposed method performs better in terms of visual effects with nearly 20% improvement in objective evaluation. Underwater image enhancement Deep learning Image processing Computer vision Zhang, Jian verfasserin aut Qin, Xin verfasserin aut Xiao, Yuchen verfasserin aut Qian, Jianjun verfasserin aut Bo, Liling verfasserin aut Zhang, Heng verfasserin aut Li, Hongran verfasserin aut Zhong, Zhaoman verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 100 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 100 |
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10.1016/j.compeleceng.2022.107822 doi (DE-627)ELV00798801X (ELSEVIER)S0045-7906(22)00119-7 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Xu, Shuai verfasserin aut Deep retinex decomposition network for underwater image enhancement 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general idea of processing low light enhancement based on retinex, we perform color balance and illumination correction on the decomposed reflectance and illumination respectively. Finally, the fused reflectance image and illumination image are produced by post-processing to get over blurring, etc. The experiments confirm that the proposed method can retain more details and edge information. Meanwhile, compared with other underwater image enhancement methods, the proposed method performs better in terms of visual effects with nearly 20% improvement in objective evaluation. Underwater image enhancement Deep learning Image processing Computer vision Zhang, Jian verfasserin aut Qin, Xin verfasserin aut Xiao, Yuchen verfasserin aut Qian, Jianjun verfasserin aut Bo, Liling verfasserin aut Zhang, Heng verfasserin aut Li, Hongran verfasserin aut Zhong, Zhaoman verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 100 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 100 |
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Xu, Shuai @@aut@@ Zhang, Jian @@aut@@ Qin, Xin @@aut@@ Xiao, Yuchen @@aut@@ Qian, Jianjun @@aut@@ Bo, Liling @@aut@@ Zhang, Heng @@aut@@ Li, Hongran @@aut@@ Zhong, Zhaoman @@aut@@ |
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620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Deep retinex decomposition network for underwater image enhancement Underwater image enhancement Deep learning Image processing Computer vision |
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Deep retinex decomposition network for underwater image enhancement |
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Deep retinex decomposition network for underwater image enhancement |
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Xu, Shuai |
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Computers & electrical engineering |
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Xu, Shuai Zhang, Jian Qin, Xin Xiao, Yuchen Qian, Jianjun Bo, Liling Zhang, Heng Li, Hongran Zhong, Zhaoman |
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10.1016/j.compeleceng.2022.107822 |
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deep retinex decomposition network for underwater image enhancement |
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Deep retinex decomposition network for underwater image enhancement |
| abstract |
This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general idea of processing low light enhancement based on retinex, we perform color balance and illumination correction on the decomposed reflectance and illumination respectively. Finally, the fused reflectance image and illumination image are produced by post-processing to get over blurring, etc. The experiments confirm that the proposed method can retain more details and edge information. Meanwhile, compared with other underwater image enhancement methods, the proposed method performs better in terms of visual effects with nearly 20% improvement in objective evaluation. |
| abstractGer |
This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general idea of processing low light enhancement based on retinex, we perform color balance and illumination correction on the decomposed reflectance and illumination respectively. Finally, the fused reflectance image and illumination image are produced by post-processing to get over blurring, etc. The experiments confirm that the proposed method can retain more details and edge information. Meanwhile, compared with other underwater image enhancement methods, the proposed method performs better in terms of visual effects with nearly 20% improvement in objective evaluation. |
| abstract_unstemmed |
This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general idea of processing low light enhancement based on retinex, we perform color balance and illumination correction on the decomposed reflectance and illumination respectively. Finally, the fused reflectance image and illumination image are produced by post-processing to get over blurring, etc. The experiments confirm that the proposed method can retain more details and edge information. Meanwhile, compared with other underwater image enhancement methods, the proposed method performs better in terms of visual effects with nearly 20% improvement in objective evaluation. |
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| title_short |
Deep retinex decomposition network for underwater image enhancement |
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Zhang, Jian Qin, Xin Xiao, Yuchen Qian, Jianjun Bo, Liling Zhang, Heng Li, Hongran Zhong, Zhaoman |
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