Single underwater image haze removal with a learning-based approach to blurriness estimation
Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image bl...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Jian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Propolis as lipid bioactive nano-carrier for topical nasal drug delivery - Rassu, Giovanna ELSEVIER, 2015, Orlando, Fla |
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| Übergeordnetes Werk: |
volume:89 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.jvcir.2022.103656 |
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ELV059563540 |
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| 520 | |a Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. | ||
| 520 | |a Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. | ||
| 650 | 7 | |a Image dehazing |2 Elsevier | |
| 650 | 7 | |a Underwater image |2 Elsevier | |
| 650 | 7 | |a Image restoration |2 Elsevier | |
| 650 | 7 | |a Image enhancement |2 Elsevier | |
| 700 | 1 | |a Wu, Hao-Tian |4 oth | |
| 700 | 1 | |a Lu, Lu |4 oth | |
| 700 | 1 | |a Luo, Xiangyang |4 oth | |
| 700 | 1 | |a Hu, Jiankun |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Academic Press |a Rassu, Giovanna ELSEVIER |t Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |d 2015 |g Orlando, Fla |w (DE-627)ELV023814993 |
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10.1016/j.jvcir.2022.103656 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001971.pica (DE-627)ELV059563540 (ELSEVIER)S1047-3203(22)00176-6 DE-627 ger DE-627 rakwb eng 540 VZ 540 VZ Chen, Jian verfasserin aut Single underwater image haze removal with a learning-based approach to blurriness estimation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. Image dehazing Elsevier Underwater image Elsevier Image restoration Elsevier Image enhancement Elsevier Wu, Hao-Tian oth Lu, Lu oth Luo, Xiangyang oth Hu, Jiankun oth Enthalten in Academic Press Rassu, Giovanna ELSEVIER Propolis as lipid bioactive nano-carrier for topical nasal drug delivery 2015 Orlando, Fla (DE-627)ELV023814993 volume:89 year:2022 pages:0 https://doi.org/10.1016/j.jvcir.2022.103656 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 AR 89 2022 0 |
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10.1016/j.jvcir.2022.103656 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001971.pica (DE-627)ELV059563540 (ELSEVIER)S1047-3203(22)00176-6 DE-627 ger DE-627 rakwb eng 540 VZ 540 VZ Chen, Jian verfasserin aut Single underwater image haze removal with a learning-based approach to blurriness estimation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. Image dehazing Elsevier Underwater image Elsevier Image restoration Elsevier Image enhancement Elsevier Wu, Hao-Tian oth Lu, Lu oth Luo, Xiangyang oth Hu, Jiankun oth Enthalten in Academic Press Rassu, Giovanna ELSEVIER Propolis as lipid bioactive nano-carrier for topical nasal drug delivery 2015 Orlando, Fla (DE-627)ELV023814993 volume:89 year:2022 pages:0 https://doi.org/10.1016/j.jvcir.2022.103656 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 AR 89 2022 0 |
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10.1016/j.jvcir.2022.103656 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001971.pica (DE-627)ELV059563540 (ELSEVIER)S1047-3203(22)00176-6 DE-627 ger DE-627 rakwb eng 540 VZ 540 VZ Chen, Jian verfasserin aut Single underwater image haze removal with a learning-based approach to blurriness estimation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. Image dehazing Elsevier Underwater image Elsevier Image restoration Elsevier Image enhancement Elsevier Wu, Hao-Tian oth Lu, Lu oth Luo, Xiangyang oth Hu, Jiankun oth Enthalten in Academic Press Rassu, Giovanna ELSEVIER Propolis as lipid bioactive nano-carrier for topical nasal drug delivery 2015 Orlando, Fla (DE-627)ELV023814993 volume:89 year:2022 pages:0 https://doi.org/10.1016/j.jvcir.2022.103656 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 AR 89 2022 0 |
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10.1016/j.jvcir.2022.103656 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001971.pica (DE-627)ELV059563540 (ELSEVIER)S1047-3203(22)00176-6 DE-627 ger DE-627 rakwb eng 540 VZ 540 VZ Chen, Jian verfasserin aut Single underwater image haze removal with a learning-based approach to blurriness estimation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. Image dehazing Elsevier Underwater image Elsevier Image restoration Elsevier Image enhancement Elsevier Wu, Hao-Tian oth Lu, Lu oth Luo, Xiangyang oth Hu, Jiankun oth Enthalten in Academic Press Rassu, Giovanna ELSEVIER Propolis as lipid bioactive nano-carrier for topical nasal drug delivery 2015 Orlando, Fla (DE-627)ELV023814993 volume:89 year:2022 pages:0 https://doi.org/10.1016/j.jvcir.2022.103656 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 AR 89 2022 0 |
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10.1016/j.jvcir.2022.103656 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001971.pica (DE-627)ELV059563540 (ELSEVIER)S1047-3203(22)00176-6 DE-627 ger DE-627 rakwb eng 540 VZ 540 VZ Chen, Jian verfasserin aut Single underwater image haze removal with a learning-based approach to blurriness estimation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. Image dehazing Elsevier Underwater image Elsevier Image restoration Elsevier Image enhancement Elsevier Wu, Hao-Tian oth Lu, Lu oth Luo, Xiangyang oth Hu, Jiankun oth Enthalten in Academic Press Rassu, Giovanna ELSEVIER Propolis as lipid bioactive nano-carrier for topical nasal drug delivery 2015 Orlando, Fla (DE-627)ELV023814993 volume:89 year:2022 pages:0 https://doi.org/10.1016/j.jvcir.2022.103656 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 AR 89 2022 0 |
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Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. |
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Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. |
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Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks. |
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Single underwater image haze removal with a learning-based approach to blurriness estimation |
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