Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond

Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range de...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wei, Debin [verfasserIn] Xie, Hongji [verfasserIn] Zhang, Zengxi [verfasserIn] Yan, Tiantian [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Underwater image enhancement Underwater object detection Contrastive learning

Übergeordnetes Werk:	Enthalten in: Journal of visual communication and image representation - Orlando, Fla. : Academic Press, 1990, 98
Übergeordnetes Werk:	volume:98

DOI / URN:	10.1016/j.jvcir.2024.104059

Katalog-ID:	ELV066955955

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520			\|a Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques.
650		4	\|a Underwater image enhancement
650		4	\|a Underwater object detection
650		4	\|a Contrastive learning
700	1		\|a Xie, Hongji \|e verfasserin \|4 aut
700	1		\|a Zhang, Zengxi \|e verfasserin \|4 aut
700	1		\|a Yan, Tiantian \|e verfasserin \|0 (orcid)0000-0002-0811-9706 \|4 aut
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matchkey_str	article:10473203:2024----::erighlsiseiiclrrnfrewtculcnrsieosritfrne
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bklnumber	54.74
publishDate	2024
allfields	10.1016/j.jvcir.2024.104059 doi (DE-627)ELV066955955 (ELSEVIER)S1047-3203(24)00014-2 DE-627 ger DE-627 rda eng 620 VZ 54.74 bkl Wei, Debin verfasserin aut Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques. Underwater image enhancement Underwater object detection Contrastive learning Xie, Hongji verfasserin aut Zhang, Zengxi verfasserin aut Yan, Tiantian verfasserin (orcid)0000-0002-0811-9706 aut Enthalten in Journal of visual communication and image representation Orlando, Fla. : Academic Press, 1990 98 Online-Ressource (DE-627)267838247 (DE-600)1470957-0 (DE-576)114818010 1047-3203 nnns volume:98 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 98
spelling	10.1016/j.jvcir.2024.104059 doi (DE-627)ELV066955955 (ELSEVIER)S1047-3203(24)00014-2 DE-627 ger DE-627 rda eng 620 VZ 54.74 bkl Wei, Debin verfasserin aut Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques. Underwater image enhancement Underwater object detection Contrastive learning Xie, Hongji verfasserin aut Zhang, Zengxi verfasserin aut Yan, Tiantian verfasserin (orcid)0000-0002-0811-9706 aut Enthalten in Journal of visual communication and image representation Orlando, Fla. : Academic Press, 1990 98 Online-Ressource (DE-627)267838247 (DE-600)1470957-0 (DE-576)114818010 1047-3203 nnns volume:98 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 98
allfields_unstemmed	10.1016/j.jvcir.2024.104059 doi (DE-627)ELV066955955 (ELSEVIER)S1047-3203(24)00014-2 DE-627 ger DE-627 rda eng 620 VZ 54.74 bkl Wei, Debin verfasserin aut Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques. Underwater image enhancement Underwater object detection Contrastive learning Xie, Hongji verfasserin aut Zhang, Zengxi verfasserin aut Yan, Tiantian verfasserin (orcid)0000-0002-0811-9706 aut Enthalten in Journal of visual communication and image representation Orlando, Fla. : Academic Press, 1990 98 Online-Ressource (DE-627)267838247 (DE-600)1470957-0 (DE-576)114818010 1047-3203 nnns volume:98 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 98
allfieldsGer	10.1016/j.jvcir.2024.104059 doi (DE-627)ELV066955955 (ELSEVIER)S1047-3203(24)00014-2 DE-627 ger DE-627 rda eng 620 VZ 54.74 bkl Wei, Debin verfasserin aut Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques. Underwater image enhancement Underwater object detection Contrastive learning Xie, Hongji verfasserin aut Zhang, Zengxi verfasserin aut Yan, Tiantian verfasserin (orcid)0000-0002-0811-9706 aut Enthalten in Journal of visual communication and image representation Orlando, Fla. : Academic Press, 1990 98 Online-Ressource (DE-627)267838247 (DE-600)1470957-0 (DE-576)114818010 1047-3203 nnns volume:98 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 98
allfieldsSound	10.1016/j.jvcir.2024.104059 doi (DE-627)ELV066955955 (ELSEVIER)S1047-3203(24)00014-2 DE-627 ger DE-627 rda eng 620 VZ 54.74 bkl Wei, Debin verfasserin aut Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques. Underwater image enhancement Underwater object detection Contrastive learning Xie, Hongji verfasserin aut Zhang, Zengxi verfasserin aut Yan, Tiantian verfasserin (orcid)0000-0002-0811-9706 aut Enthalten in Journal of visual communication and image representation Orlando, Fla. : Academic Press, 1990 98 Online-Ressource (DE-627)267838247 (DE-600)1470957-0 (DE-576)114818010 1047-3203 nnns volume:98 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 98
language	English
source	Enthalten in Journal of visual communication and image representation 98 volume:98
sourceStr	Enthalten in Journal of visual communication and image representation 98 volume:98
format_phy_str_mv	Article
bklname	Maschinelles Sehen
institution	findex.gbv.de
topic_facet	Underwater image enhancement Underwater object detection Contrastive learning
dewey-raw	620
isfreeaccess_bool	false
container_title	Journal of visual communication and image representation
authorswithroles_txt_mv	Wei, Debin @@aut@@ Xie, Hongji @@aut@@ Zhang, Zengxi @@aut@@ Yan, Tiantian @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	267838247
dewey-sort	3620
id	ELV066955955
language_de	englisch
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author	Wei, Debin
spellingShingle	Wei, Debin ddc 620 bkl 54.74 misc Underwater image enhancement misc Underwater object detection misc Contrastive learning Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond
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topic_title	620 VZ 54.74 bkl Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond Underwater image enhancement Underwater object detection Contrastive learning
topic	ddc 620 bkl 54.74 misc Underwater image enhancement misc Underwater object detection misc Contrastive learning
topic_unstemmed	ddc 620 bkl 54.74 misc Underwater image enhancement misc Underwater object detection misc Contrastive learning
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title	Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond
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title_full	Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond
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journal	Journal of visual communication and image representation
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author_browse	Wei, Debin Xie, Hongji Zhang, Zengxi Yan, Tiantian
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title_sort	learning a holistic-specific color transformer with couple contrastive constraints for underwater image enhancement and beyond
title_auth	Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond
abstract	Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques.
abstractGer	Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques.
abstract_unstemmed	Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques.
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title_short	Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond
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author2	Xie, Hongji Zhang, Zengxi Yan, Tiantian
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doi_str	10.1016/j.jvcir.2024.104059
up_date	2024-07-06T19:35:05.137Z
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