Mobile-UNet GAN: A single-image dehazing model

Abstract Dehazing, a crucial task in computer vision applications, aims to remove atmospheric haze from images. The emergence of generative adversarial networks (GANs) as a powerful solution for image restoration, including dehazing, has garnered recent attention. In this paper, an innovative approa...
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Autor*in:	Akhtar, Md Sohel [verfasserIn] Ali, Asfak Chaudhuri, Sheli Sinha

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Generative adversarial networks UNet MobileNet EfficientNet ResNet VGG19

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Signal, image and video processing - London [u.a.] : Springer, 2007, 18(2023), 1 vom: 04. Sept., Seite 275-283
Übergeordnetes Werk:	volume:18 ; year:2023 ; number:1 ; day:04 ; month:09 ; pages:275-283

Links:	Volltext

DOI / URN:	10.1007/s11760-023-02752-3

Katalog-ID:	SPR054523095

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520			\|a Abstract Dehazing, a crucial task in computer vision applications, aims to remove atmospheric haze from images. The emergence of generative adversarial networks (GANs) as a powerful solution for image restoration, including dehazing, has garnered recent attention. In this paper, an innovative approach for dehazing is introduced, utilizing a GAN based on the UNet architecture. To ensure effective extraction and encoding of vital features from hazy images, the model incorporates various encoder blocks, namely modified-MobileNet, EfficientNet, ResNet, and VGG19. Through extensive experimentation and evaluation, it is revealed that the modified-MobileNet encoder block proves to be the most efficient for the dehazing GAN. The outcomes of the experiments and comparison analyses provide strong evidence supporting the efficacy of the suggested approach in achieving high-quality dehazing results. The proposed method achieves less run-time than the existing models with run-time of 0.1 image/s.
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allfields	10.1007/s11760-023-02752-3 doi (DE-627)SPR054523095 (SPR)s11760-023-02752-3-e DE-627 ger DE-627 rakwb eng Akhtar, Md Sohel verfasserin aut Mobile-UNet GAN: A single-image dehazing model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Dehazing, a crucial task in computer vision applications, aims to remove atmospheric haze from images. The emergence of generative adversarial networks (GANs) as a powerful solution for image restoration, including dehazing, has garnered recent attention. In this paper, an innovative approach for dehazing is introduced, utilizing a GAN based on the UNet architecture. To ensure effective extraction and encoding of vital features from hazy images, the model incorporates various encoder blocks, namely modified-MobileNet, EfficientNet, ResNet, and VGG19. Through extensive experimentation and evaluation, it is revealed that the modified-MobileNet encoder block proves to be the most efficient for the dehazing GAN. The outcomes of the experiments and comparison analyses provide strong evidence supporting the efficacy of the suggested approach in achieving high-quality dehazing results. The proposed method achieves less run-time than the existing models with run-time of 0.1 image/s. Generative adversarial networks (dpeaa)DE-He213 UNet (dpeaa)DE-He213 MobileNet (dpeaa)DE-He213 EfficientNet (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 VGG19 (dpeaa)DE-He213 Ali, Asfak aut Chaudhuri, Sheli Sinha aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 04. Sept., Seite 275-283 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:04 month:09 pages:275-283 https://dx.doi.org/10.1007/s11760-023-02752-3 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_65 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_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 18 2023 1 04 09 275-283
spelling	10.1007/s11760-023-02752-3 doi (DE-627)SPR054523095 (SPR)s11760-023-02752-3-e DE-627 ger DE-627 rakwb eng Akhtar, Md Sohel verfasserin aut Mobile-UNet GAN: A single-image dehazing model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Dehazing, a crucial task in computer vision applications, aims to remove atmospheric haze from images. The emergence of generative adversarial networks (GANs) as a powerful solution for image restoration, including dehazing, has garnered recent attention. In this paper, an innovative approach for dehazing is introduced, utilizing a GAN based on the UNet architecture. To ensure effective extraction and encoding of vital features from hazy images, the model incorporates various encoder blocks, namely modified-MobileNet, EfficientNet, ResNet, and VGG19. Through extensive experimentation and evaluation, it is revealed that the modified-MobileNet encoder block proves to be the most efficient for the dehazing GAN. The outcomes of the experiments and comparison analyses provide strong evidence supporting the efficacy of the suggested approach in achieving high-quality dehazing results. The proposed method achieves less run-time than the existing models with run-time of 0.1 image/s. Generative adversarial networks (dpeaa)DE-He213 UNet (dpeaa)DE-He213 MobileNet (dpeaa)DE-He213 EfficientNet (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 VGG19 (dpeaa)DE-He213 Ali, Asfak aut Chaudhuri, Sheli Sinha aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 04. Sept., Seite 275-283 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:04 month:09 pages:275-283 https://dx.doi.org/10.1007/s11760-023-02752-3 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_65 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_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 18 2023 1 04 09 275-283
allfields_unstemmed	10.1007/s11760-023-02752-3 doi (DE-627)SPR054523095 (SPR)s11760-023-02752-3-e DE-627 ger DE-627 rakwb eng Akhtar, Md Sohel verfasserin aut Mobile-UNet GAN: A single-image dehazing model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Dehazing, a crucial task in computer vision applications, aims to remove atmospheric haze from images. The emergence of generative adversarial networks (GANs) as a powerful solution for image restoration, including dehazing, has garnered recent attention. In this paper, an innovative approach for dehazing is introduced, utilizing a GAN based on the UNet architecture. To ensure effective extraction and encoding of vital features from hazy images, the model incorporates various encoder blocks, namely modified-MobileNet, EfficientNet, ResNet, and VGG19. Through extensive experimentation and evaluation, it is revealed that the modified-MobileNet encoder block proves to be the most efficient for the dehazing GAN. The outcomes of the experiments and comparison analyses provide strong evidence supporting the efficacy of the suggested approach in achieving high-quality dehazing results. The proposed method achieves less run-time than the existing models with run-time of 0.1 image/s. Generative adversarial networks (dpeaa)DE-He213 UNet (dpeaa)DE-He213 MobileNet (dpeaa)DE-He213 EfficientNet (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 VGG19 (dpeaa)DE-He213 Ali, Asfak aut Chaudhuri, Sheli Sinha aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 04. Sept., Seite 275-283 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:04 month:09 pages:275-283 https://dx.doi.org/10.1007/s11760-023-02752-3 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_65 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_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 18 2023 1 04 09 275-283
allfieldsGer	10.1007/s11760-023-02752-3 doi (DE-627)SPR054523095 (SPR)s11760-023-02752-3-e DE-627 ger DE-627 rakwb eng Akhtar, Md Sohel verfasserin aut Mobile-UNet GAN: A single-image dehazing model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Dehazing, a crucial task in computer vision applications, aims to remove atmospheric haze from images. The emergence of generative adversarial networks (GANs) as a powerful solution for image restoration, including dehazing, has garnered recent attention. In this paper, an innovative approach for dehazing is introduced, utilizing a GAN based on the UNet architecture. To ensure effective extraction and encoding of vital features from hazy images, the model incorporates various encoder blocks, namely modified-MobileNet, EfficientNet, ResNet, and VGG19. Through extensive experimentation and evaluation, it is revealed that the modified-MobileNet encoder block proves to be the most efficient for the dehazing GAN. The outcomes of the experiments and comparison analyses provide strong evidence supporting the efficacy of the suggested approach in achieving high-quality dehazing results. The proposed method achieves less run-time than the existing models with run-time of 0.1 image/s. Generative adversarial networks (dpeaa)DE-He213 UNet (dpeaa)DE-He213 MobileNet (dpeaa)DE-He213 EfficientNet (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 VGG19 (dpeaa)DE-He213 Ali, Asfak aut Chaudhuri, Sheli Sinha aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 04. Sept., Seite 275-283 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:04 month:09 pages:275-283 https://dx.doi.org/10.1007/s11760-023-02752-3 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_65 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_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 18 2023 1 04 09 275-283
allfieldsSound	10.1007/s11760-023-02752-3 doi (DE-627)SPR054523095 (SPR)s11760-023-02752-3-e DE-627 ger DE-627 rakwb eng Akhtar, Md Sohel verfasserin aut Mobile-UNet GAN: A single-image dehazing model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Dehazing, a crucial task in computer vision applications, aims to remove atmospheric haze from images. The emergence of generative adversarial networks (GANs) as a powerful solution for image restoration, including dehazing, has garnered recent attention. In this paper, an innovative approach for dehazing is introduced, utilizing a GAN based on the UNet architecture. To ensure effective extraction and encoding of vital features from hazy images, the model incorporates various encoder blocks, namely modified-MobileNet, EfficientNet, ResNet, and VGG19. Through extensive experimentation and evaluation, it is revealed that the modified-MobileNet encoder block proves to be the most efficient for the dehazing GAN. The outcomes of the experiments and comparison analyses provide strong evidence supporting the efficacy of the suggested approach in achieving high-quality dehazing results. The proposed method achieves less run-time than the existing models with run-time of 0.1 image/s. Generative adversarial networks (dpeaa)DE-He213 UNet (dpeaa)DE-He213 MobileNet (dpeaa)DE-He213 EfficientNet (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 VGG19 (dpeaa)DE-He213 Ali, Asfak aut Chaudhuri, Sheli Sinha aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 04. Sept., Seite 275-283 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:04 month:09 pages:275-283 https://dx.doi.org/10.1007/s11760-023-02752-3 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_65 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_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 18 2023 1 04 09 275-283
language	English
source	Enthalten in Signal, image and video processing 18(2023), 1 vom: 04. Sept., Seite 275-283 volume:18 year:2023 number:1 day:04 month:09 pages:275-283
sourceStr	Enthalten in Signal, image and video processing 18(2023), 1 vom: 04. Sept., Seite 275-283 volume:18 year:2023 number:1 day:04 month:09 pages:275-283
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Generative adversarial networks UNet MobileNet EfficientNet ResNet VGG19
isfreeaccess_bool	false
container_title	Signal, image and video processing
authorswithroles_txt_mv	Akhtar, Md Sohel @@aut@@ Ali, Asfak @@aut@@ Chaudhuri, Sheli Sinha @@aut@@
publishDateDaySort_date	2023-09-04T00:00:00Z
hierarchy_top_id	546899102
id	SPR054523095
language_de	englisch
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author	Akhtar, Md Sohel
spellingShingle	Akhtar, Md Sohel misc Generative adversarial networks misc UNet misc MobileNet misc EfficientNet misc ResNet misc VGG19 Mobile-UNet GAN: A single-image dehazing model
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topic_title	Mobile-UNet GAN: A single-image dehazing model Generative adversarial networks (dpeaa)DE-He213 UNet (dpeaa)DE-He213 MobileNet (dpeaa)DE-He213 EfficientNet (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 VGG19 (dpeaa)DE-He213
topic	misc Generative adversarial networks misc UNet misc MobileNet misc EfficientNet misc ResNet misc VGG19
topic_unstemmed	misc Generative adversarial networks misc UNet misc MobileNet misc EfficientNet misc ResNet misc VGG19
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title	Mobile-UNet GAN: A single-image dehazing model
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title_full	Mobile-UNet GAN: A single-image dehazing model
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author_browse	Akhtar, Md Sohel Ali, Asfak Chaudhuri, Sheli Sinha
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title_sort	mobile-unet gan: a single-image dehazing model
title_auth	Mobile-UNet GAN: A single-image dehazing model
abstract	Abstract Dehazing, a crucial task in computer vision applications, aims to remove atmospheric haze from images. The emergence of generative adversarial networks (GANs) as a powerful solution for image restoration, including dehazing, has garnered recent attention. In this paper, an innovative approach for dehazing is introduced, utilizing a GAN based on the UNet architecture. To ensure effective extraction and encoding of vital features from hazy images, the model incorporates various encoder blocks, namely modified-MobileNet, EfficientNet, ResNet, and VGG19. Through extensive experimentation and evaluation, it is revealed that the modified-MobileNet encoder block proves to be the most efficient for the dehazing GAN. The outcomes of the experiments and comparison analyses provide strong evidence supporting the efficacy of the suggested approach in achieving high-quality dehazing results. The proposed method achieves less run-time than the existing models with run-time of 0.1 image/s. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Dehazing, a crucial task in computer vision applications, aims to remove atmospheric haze from images. The emergence of generative adversarial networks (GANs) as a powerful solution for image restoration, including dehazing, has garnered recent attention. In this paper, an innovative approach for dehazing is introduced, utilizing a GAN based on the UNet architecture. To ensure effective extraction and encoding of vital features from hazy images, the model incorporates various encoder blocks, namely modified-MobileNet, EfficientNet, ResNet, and VGG19. Through extensive experimentation and evaluation, it is revealed that the modified-MobileNet encoder block proves to be the most efficient for the dehazing GAN. The outcomes of the experiments and comparison analyses provide strong evidence supporting the efficacy of the suggested approach in achieving high-quality dehazing results. The proposed method achieves less run-time than the existing models with run-time of 0.1 image/s. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Dehazing, a crucial task in computer vision applications, aims to remove atmospheric haze from images. The emergence of generative adversarial networks (GANs) as a powerful solution for image restoration, including dehazing, has garnered recent attention. In this paper, an innovative approach for dehazing is introduced, utilizing a GAN based on the UNet architecture. To ensure effective extraction and encoding of vital features from hazy images, the model incorporates various encoder blocks, namely modified-MobileNet, EfficientNet, ResNet, and VGG19. Through extensive experimentation and evaluation, it is revealed that the modified-MobileNet encoder block proves to be the most efficient for the dehazing GAN. The outcomes of the experiments and comparison analyses provide strong evidence supporting the efficacy of the suggested approach in achieving high-quality dehazing results. The proposed method achieves less run-time than the existing models with run-time of 0.1 image/s. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Mobile-UNet GAN: A single-image dehazing model
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