DerainAttentionGAN: unsupervised single-image deraining using attention-guided generative adversarial networks
Abstract As an important task of computer vision, the single-image deraining (SID) methods tend to supervised learning in the previous research. However, most existing SID methods suffer from the inability to collect paired datasets needed by supervised learning in real scenarios. In this paper, we...
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| Autor*in: |
Guo, ZhaoKang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Signal, image and video processing - London [u.a.] : Springer, 2007, 16(2021), 1 vom: 08. Juli, Seite 185-192 |
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| Übergeordnetes Werk: |
volume:16 ; year:2021 ; number:1 ; day:08 ; month:07 ; pages:185-192 |
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| DOI / URN: |
10.1007/s11760-021-01972-9 |
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SPR046481893 |
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| 520 | |a Abstract As an important task of computer vision, the single-image deraining (SID) methods tend to supervised learning in the previous research. However, most existing SID methods suffer from the inability to collect paired datasets needed by supervised learning in real scenarios. In this paper, we introduce a recent image translation model known as CycleGAN into SID and propose Derain Attention-Guide GAN (DerainAttentionGAN) that only requires unpaired datasets can effectively overcome the above limitation. The main work of this paper is as follows: We firstly inject an attention mechanism into the generator, which makes the rain-removing regions to be concentrated near the rain line to preserve background details. Secondly, a multiscale discriminator is used to discriminate the generated image from different scales to improve its quality. Finally, the perceptual-consistency loss and internal feature perceptual loss (interfeat loss) are introduced to reduce artificial features on the generated image and make it more realistic. Experiments results demonstrate that our work is superior to the current unsupervised learning methods in terms of both quantitative and qualitative, and have achieved comparable effects to other popular supervised learning methods. | ||
| 650 | 4 | |a Single-image deraining |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Unsupervised learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a DerainAttentionGAN |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Perceptual-consistency loss |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Internal feature perceptual loss |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Hou, Mingzheng |4 aut | |
| 700 | 1 | |a Sima, Mingjun |4 aut | |
| 700 | 1 | |a Feng, ZiLiang |4 aut | |
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10.1007/s11760-021-01972-9 doi (DE-627)SPR046481893 (SPR)s11760-021-01972-9-e DE-627 ger DE-627 rakwb eng Guo, ZhaoKang verfasserin (orcid)0000-0003-1825-7421 aut DerainAttentionGAN: unsupervised single-image deraining using attention-guided generative adversarial networks 2021 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 2021 Abstract As an important task of computer vision, the single-image deraining (SID) methods tend to supervised learning in the previous research. However, most existing SID methods suffer from the inability to collect paired datasets needed by supervised learning in real scenarios. In this paper, we introduce a recent image translation model known as CycleGAN into SID and propose Derain Attention-Guide GAN (DerainAttentionGAN) that only requires unpaired datasets can effectively overcome the above limitation. The main work of this paper is as follows: We firstly inject an attention mechanism into the generator, which makes the rain-removing regions to be concentrated near the rain line to preserve background details. Secondly, a multiscale discriminator is used to discriminate the generated image from different scales to improve its quality. Finally, the perceptual-consistency loss and internal feature perceptual loss (interfeat loss) are introduced to reduce artificial features on the generated image and make it more realistic. Experiments results demonstrate that our work is superior to the current unsupervised learning methods in terms of both quantitative and qualitative, and have achieved comparable effects to other popular supervised learning methods. Single-image deraining (dpeaa)DE-He213 Unsupervised learning (dpeaa)DE-He213 DerainAttentionGAN (dpeaa)DE-He213 Perceptual-consistency loss (dpeaa)DE-He213 Internal feature perceptual loss (dpeaa)DE-He213 Hou, Mingzheng aut Sima, Mingjun aut Feng, ZiLiang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 16(2021), 1 vom: 08. Juli, Seite 185-192 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:16 year:2021 number:1 day:08 month:07 pages:185-192 https://dx.doi.org/10.1007/s11760-021-01972-9 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 16 2021 1 08 07 185-192 |
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10.1007/s11760-021-01972-9 doi (DE-627)SPR046481893 (SPR)s11760-021-01972-9-e DE-627 ger DE-627 rakwb eng Guo, ZhaoKang verfasserin (orcid)0000-0003-1825-7421 aut DerainAttentionGAN: unsupervised single-image deraining using attention-guided generative adversarial networks 2021 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 2021 Abstract As an important task of computer vision, the single-image deraining (SID) methods tend to supervised learning in the previous research. However, most existing SID methods suffer from the inability to collect paired datasets needed by supervised learning in real scenarios. In this paper, we introduce a recent image translation model known as CycleGAN into SID and propose Derain Attention-Guide GAN (DerainAttentionGAN) that only requires unpaired datasets can effectively overcome the above limitation. The main work of this paper is as follows: We firstly inject an attention mechanism into the generator, which makes the rain-removing regions to be concentrated near the rain line to preserve background details. Secondly, a multiscale discriminator is used to discriminate the generated image from different scales to improve its quality. Finally, the perceptual-consistency loss and internal feature perceptual loss (interfeat loss) are introduced to reduce artificial features on the generated image and make it more realistic. Experiments results demonstrate that our work is superior to the current unsupervised learning methods in terms of both quantitative and qualitative, and have achieved comparable effects to other popular supervised learning methods. Single-image deraining (dpeaa)DE-He213 Unsupervised learning (dpeaa)DE-He213 DerainAttentionGAN (dpeaa)DE-He213 Perceptual-consistency loss (dpeaa)DE-He213 Internal feature perceptual loss (dpeaa)DE-He213 Hou, Mingzheng aut Sima, Mingjun aut Feng, ZiLiang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 16(2021), 1 vom: 08. Juli, Seite 185-192 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:16 year:2021 number:1 day:08 month:07 pages:185-192 https://dx.doi.org/10.1007/s11760-021-01972-9 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 16 2021 1 08 07 185-192 |
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10.1007/s11760-021-01972-9 doi (DE-627)SPR046481893 (SPR)s11760-021-01972-9-e DE-627 ger DE-627 rakwb eng Guo, ZhaoKang verfasserin (orcid)0000-0003-1825-7421 aut DerainAttentionGAN: unsupervised single-image deraining using attention-guided generative adversarial networks 2021 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 2021 Abstract As an important task of computer vision, the single-image deraining (SID) methods tend to supervised learning in the previous research. However, most existing SID methods suffer from the inability to collect paired datasets needed by supervised learning in real scenarios. In this paper, we introduce a recent image translation model known as CycleGAN into SID and propose Derain Attention-Guide GAN (DerainAttentionGAN) that only requires unpaired datasets can effectively overcome the above limitation. The main work of this paper is as follows: We firstly inject an attention mechanism into the generator, which makes the rain-removing regions to be concentrated near the rain line to preserve background details. Secondly, a multiscale discriminator is used to discriminate the generated image from different scales to improve its quality. Finally, the perceptual-consistency loss and internal feature perceptual loss (interfeat loss) are introduced to reduce artificial features on the generated image and make it more realistic. Experiments results demonstrate that our work is superior to the current unsupervised learning methods in terms of both quantitative and qualitative, and have achieved comparable effects to other popular supervised learning methods. Single-image deraining (dpeaa)DE-He213 Unsupervised learning (dpeaa)DE-He213 DerainAttentionGAN (dpeaa)DE-He213 Perceptual-consistency loss (dpeaa)DE-He213 Internal feature perceptual loss (dpeaa)DE-He213 Hou, Mingzheng aut Sima, Mingjun aut Feng, ZiLiang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 16(2021), 1 vom: 08. Juli, Seite 185-192 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:16 year:2021 number:1 day:08 month:07 pages:185-192 https://dx.doi.org/10.1007/s11760-021-01972-9 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 16 2021 1 08 07 185-192 |
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10.1007/s11760-021-01972-9 doi (DE-627)SPR046481893 (SPR)s11760-021-01972-9-e DE-627 ger DE-627 rakwb eng Guo, ZhaoKang verfasserin (orcid)0000-0003-1825-7421 aut DerainAttentionGAN: unsupervised single-image deraining using attention-guided generative adversarial networks 2021 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 2021 Abstract As an important task of computer vision, the single-image deraining (SID) methods tend to supervised learning in the previous research. However, most existing SID methods suffer from the inability to collect paired datasets needed by supervised learning in real scenarios. In this paper, we introduce a recent image translation model known as CycleGAN into SID and propose Derain Attention-Guide GAN (DerainAttentionGAN) that only requires unpaired datasets can effectively overcome the above limitation. The main work of this paper is as follows: We firstly inject an attention mechanism into the generator, which makes the rain-removing regions to be concentrated near the rain line to preserve background details. Secondly, a multiscale discriminator is used to discriminate the generated image from different scales to improve its quality. Finally, the perceptual-consistency loss and internal feature perceptual loss (interfeat loss) are introduced to reduce artificial features on the generated image and make it more realistic. Experiments results demonstrate that our work is superior to the current unsupervised learning methods in terms of both quantitative and qualitative, and have achieved comparable effects to other popular supervised learning methods. Single-image deraining (dpeaa)DE-He213 Unsupervised learning (dpeaa)DE-He213 DerainAttentionGAN (dpeaa)DE-He213 Perceptual-consistency loss (dpeaa)DE-He213 Internal feature perceptual loss (dpeaa)DE-He213 Hou, Mingzheng aut Sima, Mingjun aut Feng, ZiLiang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 16(2021), 1 vom: 08. Juli, Seite 185-192 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:16 year:2021 number:1 day:08 month:07 pages:185-192 https://dx.doi.org/10.1007/s11760-021-01972-9 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 16 2021 1 08 07 185-192 |
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10.1007/s11760-021-01972-9 doi (DE-627)SPR046481893 (SPR)s11760-021-01972-9-e DE-627 ger DE-627 rakwb eng Guo, ZhaoKang verfasserin (orcid)0000-0003-1825-7421 aut DerainAttentionGAN: unsupervised single-image deraining using attention-guided generative adversarial networks 2021 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 2021 Abstract As an important task of computer vision, the single-image deraining (SID) methods tend to supervised learning in the previous research. However, most existing SID methods suffer from the inability to collect paired datasets needed by supervised learning in real scenarios. In this paper, we introduce a recent image translation model known as CycleGAN into SID and propose Derain Attention-Guide GAN (DerainAttentionGAN) that only requires unpaired datasets can effectively overcome the above limitation. The main work of this paper is as follows: We firstly inject an attention mechanism into the generator, which makes the rain-removing regions to be concentrated near the rain line to preserve background details. Secondly, a multiscale discriminator is used to discriminate the generated image from different scales to improve its quality. Finally, the perceptual-consistency loss and internal feature perceptual loss (interfeat loss) are introduced to reduce artificial features on the generated image and make it more realistic. Experiments results demonstrate that our work is superior to the current unsupervised learning methods in terms of both quantitative and qualitative, and have achieved comparable effects to other popular supervised learning methods. Single-image deraining (dpeaa)DE-He213 Unsupervised learning (dpeaa)DE-He213 DerainAttentionGAN (dpeaa)DE-He213 Perceptual-consistency loss (dpeaa)DE-He213 Internal feature perceptual loss (dpeaa)DE-He213 Hou, Mingzheng aut Sima, Mingjun aut Feng, ZiLiang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 16(2021), 1 vom: 08. Juli, Seite 185-192 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:16 year:2021 number:1 day:08 month:07 pages:185-192 https://dx.doi.org/10.1007/s11760-021-01972-9 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 16 2021 1 08 07 185-192 |
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DerainAttentionGAN: unsupervised single-image deraining using attention-guided generative adversarial networks Single-image deraining (dpeaa)DE-He213 Unsupervised learning (dpeaa)DE-He213 DerainAttentionGAN (dpeaa)DE-He213 Perceptual-consistency loss (dpeaa)DE-He213 Internal feature perceptual loss (dpeaa)DE-He213 |
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derainattentiongan: unsupervised single-image deraining using attention-guided generative adversarial networks |
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DerainAttentionGAN: unsupervised single-image deraining using attention-guided generative adversarial networks |
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Abstract As an important task of computer vision, the single-image deraining (SID) methods tend to supervised learning in the previous research. However, most existing SID methods suffer from the inability to collect paired datasets needed by supervised learning in real scenarios. In this paper, we introduce a recent image translation model known as CycleGAN into SID and propose Derain Attention-Guide GAN (DerainAttentionGAN) that only requires unpaired datasets can effectively overcome the above limitation. The main work of this paper is as follows: We firstly inject an attention mechanism into the generator, which makes the rain-removing regions to be concentrated near the rain line to preserve background details. Secondly, a multiscale discriminator is used to discriminate the generated image from different scales to improve its quality. Finally, the perceptual-consistency loss and internal feature perceptual loss (interfeat loss) are introduced to reduce artificial features on the generated image and make it more realistic. Experiments results demonstrate that our work is superior to the current unsupervised learning methods in terms of both quantitative and qualitative, and have achieved comparable effects to other popular supervised learning methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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Abstract As an important task of computer vision, the single-image deraining (SID) methods tend to supervised learning in the previous research. However, most existing SID methods suffer from the inability to collect paired datasets needed by supervised learning in real scenarios. In this paper, we introduce a recent image translation model known as CycleGAN into SID and propose Derain Attention-Guide GAN (DerainAttentionGAN) that only requires unpaired datasets can effectively overcome the above limitation. The main work of this paper is as follows: We firstly inject an attention mechanism into the generator, which makes the rain-removing regions to be concentrated near the rain line to preserve background details. Secondly, a multiscale discriminator is used to discriminate the generated image from different scales to improve its quality. Finally, the perceptual-consistency loss and internal feature perceptual loss (interfeat loss) are introduced to reduce artificial features on the generated image and make it more realistic. Experiments results demonstrate that our work is superior to the current unsupervised learning methods in terms of both quantitative and qualitative, and have achieved comparable effects to other popular supervised learning methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract As an important task of computer vision, the single-image deraining (SID) methods tend to supervised learning in the previous research. However, most existing SID methods suffer from the inability to collect paired datasets needed by supervised learning in real scenarios. In this paper, we introduce a recent image translation model known as CycleGAN into SID and propose Derain Attention-Guide GAN (DerainAttentionGAN) that only requires unpaired datasets can effectively overcome the above limitation. The main work of this paper is as follows: We firstly inject an attention mechanism into the generator, which makes the rain-removing regions to be concentrated near the rain line to preserve background details. Secondly, a multiscale discriminator is used to discriminate the generated image from different scales to improve its quality. Finally, the perceptual-consistency loss and internal feature perceptual loss (interfeat loss) are introduced to reduce artificial features on the generated image and make it more realistic. Experiments results demonstrate that our work is superior to the current unsupervised learning methods in terms of both quantitative and qualitative, and have achieved comparable effects to other popular supervised learning methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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| title_short |
DerainAttentionGAN: unsupervised single-image deraining using attention-guided generative adversarial networks |
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https://dx.doi.org/10.1007/s11760-021-01972-9 |
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Hou, Mingzheng Sima, Mingjun Feng, ZiLiang |
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Hou, Mingzheng Sima, Mingjun Feng, ZiLiang |
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10.1007/s11760-021-01972-9 |
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2024-07-03T22:48:13.775Z |
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| score |
7.4008465 |