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
Autor*in: |
Chen, Jian [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022transfer abstract |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
Enthalten in: Propolis as lipid bioactive nano-carrier for topical nasal drug delivery - Rassu, Giovanna ELSEVIER, 2015, Orlando, Fla |
---|---|
Übergeordnetes Werk: |
volume:89 ; year:2022 ; pages:0 |
Links: |
---|
DOI / URN: |
10.1016/j.jvcir.2022.103656 |
---|
Katalog-ID: |
ELV059563540 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV059563540 | ||
003 | DE-627 | ||
005 | 20230626053230.0 | ||
007 | cr uuu---uuuuu | ||
008 | 221219s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.jvcir.2022.103656 |2 doi | |
028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001971.pica |
035 | |a (DE-627)ELV059563540 | ||
035 | |a (ELSEVIER)S1047-3203(22)00176-6 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 540 |q VZ |
082 | 0 | 4 | |a 540 |q VZ |
100 | 1 | |a Chen, Jian |e verfasserin |4 aut | |
245 | 1 | 0 | |a Single underwater image haze removal with a learning-based approach to blurriness estimation |
264 | 1 | |c 2022transfer abstract | |
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a nicht spezifiziert |b z |2 rdamedia | ||
338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
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 |
773 | 1 | 8 | |g volume:89 |g year:2022 |g pages:0 |
856 | 4 | 0 | |u https://doi.org/10.1016/j.jvcir.2022.103656 |3 Volltext |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_21 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_50 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_72 | ||
912 | |a GBV_ILN_136 | ||
912 | |a GBV_ILN_162 | ||
912 | |a GBV_ILN_165 | ||
912 | |a GBV_ILN_176 | ||
912 | |a GBV_ILN_181 | ||
912 | |a GBV_ILN_203 | ||
912 | |a GBV_ILN_227 | ||
912 | |a GBV_ILN_352 | ||
912 | |a GBV_ILN_676 | ||
912 | |a GBV_ILN_791 | ||
912 | |a GBV_ILN_1018 | ||
951 | |a AR | ||
952 | |d 89 |j 2022 |h 0 |
author_variant |
j c jc |
---|---|
matchkey_str |
chenjianwuhaotianlululuoxiangyanghujiank:2022----:igenewtrmghzrmvlihlannbsdpraho |
hierarchy_sort_str |
2022transfer abstract |
publishDate |
2022 |
allfields |
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 |
spelling |
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 |
allfields_unstemmed |
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 |
allfieldsGer |
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 |
allfieldsSound |
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 |
language |
English |
source |
Enthalten in Propolis as lipid bioactive nano-carrier for topical nasal drug delivery Orlando, Fla volume:89 year:2022 pages:0 |
sourceStr |
Enthalten in Propolis as lipid bioactive nano-carrier for topical nasal drug delivery Orlando, Fla volume:89 year:2022 pages:0 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Image dehazing Underwater image Image restoration Image enhancement |
dewey-raw |
540 |
isfreeaccess_bool |
false |
container_title |
Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |
authorswithroles_txt_mv |
Chen, Jian @@aut@@ Wu, Hao-Tian @@oth@@ Lu, Lu @@oth@@ Luo, Xiangyang @@oth@@ Hu, Jiankun @@oth@@ |
publishDateDaySort_date |
2022-01-01T00:00:00Z |
hierarchy_top_id |
ELV023814993 |
dewey-sort |
3540 |
id |
ELV059563540 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV059563540</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626053230.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221219s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jvcir.2022.103656</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001971.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059563540</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1047-3203(22)00176-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chen, Jian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Single underwater image haze removal with a learning-based approach to blurriness estimation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Image dehazing</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Underwater image</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Image restoration</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Image enhancement</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Hao-Tian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lu, Lu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Xiangyang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Jiankun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="a">Rassu, Giovanna ELSEVIER</subfield><subfield code="t">Propolis as lipid bioactive nano-carrier for topical nasal drug delivery</subfield><subfield code="d">2015</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV023814993</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:89</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jvcir.2022.103656</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_50</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_72</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_136</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_162</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_165</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_176</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_181</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_203</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_227</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_352</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_676</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_791</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_1018</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">89</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
author |
Chen, Jian |
spellingShingle |
Chen, Jian ddc 540 Elsevier Image dehazing Elsevier Underwater image Elsevier Image restoration Elsevier Image enhancement Single underwater image haze removal with a learning-based approach to blurriness estimation |
authorStr |
Chen, Jian |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV023814993 |
format |
electronic Article |
dewey-ones |
540 - Chemistry & allied sciences |
delete_txt_mv |
keep |
author_role |
aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
540 VZ Single underwater image haze removal with a learning-based approach to blurriness estimation Image dehazing Elsevier Underwater image Elsevier Image restoration Elsevier Image enhancement Elsevier |
topic |
ddc 540 Elsevier Image dehazing Elsevier Underwater image Elsevier Image restoration Elsevier Image enhancement |
topic_unstemmed |
ddc 540 Elsevier Image dehazing Elsevier Underwater image Elsevier Image restoration Elsevier Image enhancement |
topic_browse |
ddc 540 Elsevier Image dehazing Elsevier Underwater image Elsevier Image restoration Elsevier Image enhancement |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
zu |
author2_variant |
h t w htw l l ll x l xl j h jh |
hierarchy_parent_title |
Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |
hierarchy_parent_id |
ELV023814993 |
dewey-tens |
540 - Chemistry |
hierarchy_top_title |
Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)ELV023814993 |
title |
Single underwater image haze removal with a learning-based approach to blurriness estimation |
ctrlnum |
(DE-627)ELV059563540 (ELSEVIER)S1047-3203(22)00176-6 |
title_full |
Single underwater image haze removal with a learning-based approach to blurriness estimation |
author_sort |
Chen, Jian |
journal |
Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |
journalStr |
Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
500 - Science |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
zzz |
container_start_page |
0 |
author_browse |
Chen, Jian |
container_volume |
89 |
class |
540 VZ |
format_se |
Elektronische Aufsätze |
author-letter |
Chen, Jian |
doi_str_mv |
10.1016/j.jvcir.2022.103656 |
dewey-full |
540 |
title_sort |
single underwater image haze removal with a learning-based approach to blurriness estimation |
title_auth |
Single underwater image haze removal with a learning-based approach to blurriness estimation |
abstract |
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. |
abstractGer |
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. |
abstract_unstemmed |
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. |
collection_details |
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 |
title_short |
Single underwater image haze removal with a learning-based approach to blurriness estimation |
url |
https://doi.org/10.1016/j.jvcir.2022.103656 |
remote_bool |
true |
author2 |
Wu, Hao-Tian Lu, Lu Luo, Xiangyang Hu, Jiankun |
author2Str |
Wu, Hao-Tian Lu, Lu Luo, Xiangyang Hu, Jiankun |
ppnlink |
ELV023814993 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth oth |
doi_str |
10.1016/j.jvcir.2022.103656 |
up_date |
2024-07-06T22:22:36.059Z |
_version_ |
1803870070176219136 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV059563540</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626053230.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221219s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jvcir.2022.103656</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001971.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059563540</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1047-3203(22)00176-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chen, Jian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Single underwater image haze removal with a learning-based approach to blurriness estimation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Image dehazing</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Underwater image</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Image restoration</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Image enhancement</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Hao-Tian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lu, Lu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Xiangyang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Jiankun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="a">Rassu, Giovanna ELSEVIER</subfield><subfield code="t">Propolis as lipid bioactive nano-carrier for topical nasal drug delivery</subfield><subfield code="d">2015</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV023814993</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:89</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jvcir.2022.103656</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_50</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_72</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_136</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_162</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_165</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_176</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_181</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_203</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_227</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_352</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_676</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_791</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_1018</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">89</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
score |
7.400667 |