Real-time image and video dehazing based on multiscale guided filtering

Abstract We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Van Nguyen, Thuong [verfasserIn] Vien, An Gia Lee, Chul

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Image dehazing Image enhancement Image restoration Guided image filtering

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 81(2022), 25 vom: 13. Aug., Seite 36567-36584
Übergeordnetes Werk:	volume:81 ; year:2022 ; number:25 ; day:13 ; month:08 ; pages:36567-36584

Links:	Volltext

DOI / URN:	10.1007/s11042-022-13533-4

Katalog-ID:	SPR048199826

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allfields	10.1007/s11042-022-13533-4 doi (DE-627)SPR048199826 (SPR)s11042-022-13533-4-e DE-627 ger DE-627 rakwb eng Van Nguyen, Thuong verfasserin aut Real-time image and video dehazing based on multiscale guided filtering 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach for the estimation. First, we construct an image pyramid from a hazy image. Then, we estimate the transmission map and atmospheric light at the coarsest level. Next, we obtain the transmission at the finest level by iterative upsampling with guide image filtering to avoid information loss. Furthermore, we extend the single-image dehazing algorithm to real-time video dehazing to reduce flickering artifacts in dehazed videos by making transmission values temporally coherent. Experimental results show that the proposed algorithm is applicable in real-time applications, while providing comparable or even better performance than that of state-of-the-art algorithms. Image dehazing (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Guided image filtering (dpeaa)DE-He213 Vien, An Gia aut Lee, Chul (orcid)0000-0001-9329-7365 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 25 vom: 13. Aug., Seite 36567-36584 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:25 day:13 month:08 pages:36567-36584 https://dx.doi.org/10.1007/s11042-022-13533-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 25 13 08 36567-36584
spelling	10.1007/s11042-022-13533-4 doi (DE-627)SPR048199826 (SPR)s11042-022-13533-4-e DE-627 ger DE-627 rakwb eng Van Nguyen, Thuong verfasserin aut Real-time image and video dehazing based on multiscale guided filtering 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach for the estimation. First, we construct an image pyramid from a hazy image. Then, we estimate the transmission map and atmospheric light at the coarsest level. Next, we obtain the transmission at the finest level by iterative upsampling with guide image filtering to avoid information loss. Furthermore, we extend the single-image dehazing algorithm to real-time video dehazing to reduce flickering artifacts in dehazed videos by making transmission values temporally coherent. Experimental results show that the proposed algorithm is applicable in real-time applications, while providing comparable or even better performance than that of state-of-the-art algorithms. Image dehazing (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Guided image filtering (dpeaa)DE-He213 Vien, An Gia aut Lee, Chul (orcid)0000-0001-9329-7365 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 25 vom: 13. Aug., Seite 36567-36584 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:25 day:13 month:08 pages:36567-36584 https://dx.doi.org/10.1007/s11042-022-13533-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 25 13 08 36567-36584
allfields_unstemmed	10.1007/s11042-022-13533-4 doi (DE-627)SPR048199826 (SPR)s11042-022-13533-4-e DE-627 ger DE-627 rakwb eng Van Nguyen, Thuong verfasserin aut Real-time image and video dehazing based on multiscale guided filtering 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach for the estimation. First, we construct an image pyramid from a hazy image. Then, we estimate the transmission map and atmospheric light at the coarsest level. Next, we obtain the transmission at the finest level by iterative upsampling with guide image filtering to avoid information loss. Furthermore, we extend the single-image dehazing algorithm to real-time video dehazing to reduce flickering artifacts in dehazed videos by making transmission values temporally coherent. Experimental results show that the proposed algorithm is applicable in real-time applications, while providing comparable or even better performance than that of state-of-the-art algorithms. Image dehazing (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Guided image filtering (dpeaa)DE-He213 Vien, An Gia aut Lee, Chul (orcid)0000-0001-9329-7365 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 25 vom: 13. Aug., Seite 36567-36584 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:25 day:13 month:08 pages:36567-36584 https://dx.doi.org/10.1007/s11042-022-13533-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 25 13 08 36567-36584
allfieldsGer	10.1007/s11042-022-13533-4 doi (DE-627)SPR048199826 (SPR)s11042-022-13533-4-e DE-627 ger DE-627 rakwb eng Van Nguyen, Thuong verfasserin aut Real-time image and video dehazing based on multiscale guided filtering 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach for the estimation. First, we construct an image pyramid from a hazy image. Then, we estimate the transmission map and atmospheric light at the coarsest level. Next, we obtain the transmission at the finest level by iterative upsampling with guide image filtering to avoid information loss. Furthermore, we extend the single-image dehazing algorithm to real-time video dehazing to reduce flickering artifacts in dehazed videos by making transmission values temporally coherent. Experimental results show that the proposed algorithm is applicable in real-time applications, while providing comparable or even better performance than that of state-of-the-art algorithms. Image dehazing (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Guided image filtering (dpeaa)DE-He213 Vien, An Gia aut Lee, Chul (orcid)0000-0001-9329-7365 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 25 vom: 13. Aug., Seite 36567-36584 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:25 day:13 month:08 pages:36567-36584 https://dx.doi.org/10.1007/s11042-022-13533-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 25 13 08 36567-36584
allfieldsSound	10.1007/s11042-022-13533-4 doi (DE-627)SPR048199826 (SPR)s11042-022-13533-4-e DE-627 ger DE-627 rakwb eng Van Nguyen, Thuong verfasserin aut Real-time image and video dehazing based on multiscale guided filtering 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach for the estimation. First, we construct an image pyramid from a hazy image. Then, we estimate the transmission map and atmospheric light at the coarsest level. Next, we obtain the transmission at the finest level by iterative upsampling with guide image filtering to avoid information loss. Furthermore, we extend the single-image dehazing algorithm to real-time video dehazing to reduce flickering artifacts in dehazed videos by making transmission values temporally coherent. Experimental results show that the proposed algorithm is applicable in real-time applications, while providing comparable or even better performance than that of state-of-the-art algorithms. Image dehazing (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Guided image filtering (dpeaa)DE-He213 Vien, An Gia aut Lee, Chul (orcid)0000-0001-9329-7365 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 25 vom: 13. Aug., Seite 36567-36584 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:25 day:13 month:08 pages:36567-36584 https://dx.doi.org/10.1007/s11042-022-13533-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 25 13 08 36567-36584
language	English
source	Enthalten in Multimedia tools and applications 81(2022), 25 vom: 13. Aug., Seite 36567-36584 volume:81 year:2022 number:25 day:13 month:08 pages:36567-36584
sourceStr	Enthalten in Multimedia tools and applications 81(2022), 25 vom: 13. Aug., Seite 36567-36584 volume:81 year:2022 number:25 day:13 month:08 pages:36567-36584
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Image dehazing Image enhancement Image restoration Guided image filtering
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Van Nguyen, Thuong @@aut@@ Vien, An Gia @@aut@@ Lee, Chul @@aut@@
publishDateDaySort_date	2022-08-13T00:00:00Z
hierarchy_top_id	27135030X
id	SPR048199826
language_de	englisch
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author	Van Nguyen, Thuong
spellingShingle	Van Nguyen, Thuong misc Image dehazing misc Image enhancement misc Image restoration misc Guided image filtering Real-time image and video dehazing based on multiscale guided filtering
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topic_title	Real-time image and video dehazing based on multiscale guided filtering Image dehazing (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Image restoration (dpeaa)DE-He213 Guided image filtering (dpeaa)DE-He213
topic	misc Image dehazing misc Image enhancement misc Image restoration misc Guided image filtering
topic_unstemmed	misc Image dehazing misc Image enhancement misc Image restoration misc Guided image filtering
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title	Real-time image and video dehazing based on multiscale guided filtering
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title_full	Real-time image and video dehazing based on multiscale guided filtering
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title_sort	real-time image and video dehazing based on multiscale guided filtering
title_auth	Real-time image and video dehazing based on multiscale guided filtering
abstract	Abstract We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach for the estimation. First, we construct an image pyramid from a hazy image. Then, we estimate the transmission map and atmospheric light at the coarsest level. Next, we obtain the transmission at the finest level by iterative upsampling with guide image filtering to avoid information loss. Furthermore, we extend the single-image dehazing algorithm to real-time video dehazing to reduce flickering artifacts in dehazed videos by making transmission values temporally coherent. Experimental results show that the proposed algorithm is applicable in real-time applications, while providing comparable or even better performance than that of state-of-the-art algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstractGer	Abstract We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach for the estimation. First, we construct an image pyramid from a hazy image. Then, we estimate the transmission map and atmospheric light at the coarsest level. Next, we obtain the transmission at the finest level by iterative upsampling with guide image filtering to avoid information loss. Furthermore, we extend the single-image dehazing algorithm to real-time video dehazing to reduce flickering artifacts in dehazed videos by making transmission values temporally coherent. Experimental results show that the proposed algorithm is applicable in real-time applications, while providing comparable or even better performance than that of state-of-the-art algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstract_unstemmed	Abstract We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach for the estimation. First, we construct an image pyramid from a hazy image. Then, we estimate the transmission map and atmospheric light at the coarsest level. Next, we obtain the transmission at the finest level by iterative upsampling with guide image filtering to avoid information loss. Furthermore, we extend the single-image dehazing algorithm to real-time video dehazing to reduce flickering artifacts in dehazed videos by making transmission values temporally coherent. Experimental results show that the proposed algorithm is applicable in real-time applications, while providing comparable or even better performance than that of state-of-the-art algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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container_issue	25
title_short	Real-time image and video dehazing based on multiscale guided filtering
url	https://dx.doi.org/10.1007/s11042-022-13533-4
remote_bool	true
author2	Vien, An Gia Lee, Chul
author2Str	Vien, An Gia Lee, Chul
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doi_str	10.1007/s11042-022-13533-4
up_date	2024-07-03T17:40:03.586Z
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Real-time image and video dehazing based on multiscale guided filtering

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