Single image dehazing based on multi-scale segmentation and deep learning

Abstract Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in t...
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Autor*in:	Yu, Tianhe [verfasserIn] Zhu, Ming Chen, Haiming

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Image dehazing Multi-scale convolution Multi-scale segmentation Deep learning Atmospheric scattering model

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Machine vision and applications - Berlin : Springer, 1988, 33(2022), 2 vom: 22. Feb.
Übergeordnetes Werk:	volume:33 ; year:2022 ; number:2 ; day:22 ; month:02

Links:	Volltext

DOI / URN:	10.1007/s00138-022-01285-y

Katalog-ID:	SPR046303839

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520			\|a Abstract Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods.
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allfields	10.1007/s00138-022-01285-y doi (DE-627)SPR046303839 (SPR)s00138-022-01285-y-e DE-627 ger DE-627 rakwb eng Yu, Tianhe verfasserin (orcid)0000-0001-8121-7620 aut Single image dehazing based on multi-scale segmentation and deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods. Image dehazing (dpeaa)DE-He213 Multi-scale convolution (dpeaa)DE-He213 Multi-scale segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Atmospheric scattering model (dpeaa)DE-He213 Zhu, Ming aut Chen, Haiming aut Enthalten in Machine vision and applications Berlin : Springer, 1988 33(2022), 2 vom: 22. Feb. (DE-627)300186312 (DE-600)1481698-2 1432-1769 nnns volume:33 year:2022 number:2 day:22 month:02 https://dx.doi.org/10.1007/s00138-022-01285-y 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 33 2022 2 22 02
spelling	10.1007/s00138-022-01285-y doi (DE-627)SPR046303839 (SPR)s00138-022-01285-y-e DE-627 ger DE-627 rakwb eng Yu, Tianhe verfasserin (orcid)0000-0001-8121-7620 aut Single image dehazing based on multi-scale segmentation and deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods. Image dehazing (dpeaa)DE-He213 Multi-scale convolution (dpeaa)DE-He213 Multi-scale segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Atmospheric scattering model (dpeaa)DE-He213 Zhu, Ming aut Chen, Haiming aut Enthalten in Machine vision and applications Berlin : Springer, 1988 33(2022), 2 vom: 22. Feb. (DE-627)300186312 (DE-600)1481698-2 1432-1769 nnns volume:33 year:2022 number:2 day:22 month:02 https://dx.doi.org/10.1007/s00138-022-01285-y 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 33 2022 2 22 02
allfields_unstemmed	10.1007/s00138-022-01285-y doi (DE-627)SPR046303839 (SPR)s00138-022-01285-y-e DE-627 ger DE-627 rakwb eng Yu, Tianhe verfasserin (orcid)0000-0001-8121-7620 aut Single image dehazing based on multi-scale segmentation and deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods. Image dehazing (dpeaa)DE-He213 Multi-scale convolution (dpeaa)DE-He213 Multi-scale segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Atmospheric scattering model (dpeaa)DE-He213 Zhu, Ming aut Chen, Haiming aut Enthalten in Machine vision and applications Berlin : Springer, 1988 33(2022), 2 vom: 22. Feb. (DE-627)300186312 (DE-600)1481698-2 1432-1769 nnns volume:33 year:2022 number:2 day:22 month:02 https://dx.doi.org/10.1007/s00138-022-01285-y 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 33 2022 2 22 02
allfieldsGer	10.1007/s00138-022-01285-y doi (DE-627)SPR046303839 (SPR)s00138-022-01285-y-e DE-627 ger DE-627 rakwb eng Yu, Tianhe verfasserin (orcid)0000-0001-8121-7620 aut Single image dehazing based on multi-scale segmentation and deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods. Image dehazing (dpeaa)DE-He213 Multi-scale convolution (dpeaa)DE-He213 Multi-scale segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Atmospheric scattering model (dpeaa)DE-He213 Zhu, Ming aut Chen, Haiming aut Enthalten in Machine vision and applications Berlin : Springer, 1988 33(2022), 2 vom: 22. Feb. (DE-627)300186312 (DE-600)1481698-2 1432-1769 nnns volume:33 year:2022 number:2 day:22 month:02 https://dx.doi.org/10.1007/s00138-022-01285-y 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 33 2022 2 22 02
allfieldsSound	10.1007/s00138-022-01285-y doi (DE-627)SPR046303839 (SPR)s00138-022-01285-y-e DE-627 ger DE-627 rakwb eng Yu, Tianhe verfasserin (orcid)0000-0001-8121-7620 aut Single image dehazing based on multi-scale segmentation and deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods. Image dehazing (dpeaa)DE-He213 Multi-scale convolution (dpeaa)DE-He213 Multi-scale segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Atmospheric scattering model (dpeaa)DE-He213 Zhu, Ming aut Chen, Haiming aut Enthalten in Machine vision and applications Berlin : Springer, 1988 33(2022), 2 vom: 22. Feb. (DE-627)300186312 (DE-600)1481698-2 1432-1769 nnns volume:33 year:2022 number:2 day:22 month:02 https://dx.doi.org/10.1007/s00138-022-01285-y 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 33 2022 2 22 02
language	English
source	Enthalten in Machine vision and applications 33(2022), 2 vom: 22. Feb. volume:33 year:2022 number:2 day:22 month:02
sourceStr	Enthalten in Machine vision and applications 33(2022), 2 vom: 22. Feb. volume:33 year:2022 number:2 day:22 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Image dehazing Multi-scale convolution Multi-scale segmentation Deep learning Atmospheric scattering model
isfreeaccess_bool	false
container_title	Machine vision and applications
authorswithroles_txt_mv	Yu, Tianhe @@aut@@ Zhu, Ming @@aut@@ Chen, Haiming @@aut@@
publishDateDaySort_date	2022-02-22T00:00:00Z
hierarchy_top_id	300186312
id	SPR046303839
language_de	englisch
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author	Yu, Tianhe
spellingShingle	Yu, Tianhe misc Image dehazing misc Multi-scale convolution misc Multi-scale segmentation misc Deep learning misc Atmospheric scattering model Single image dehazing based on multi-scale segmentation and deep learning
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topic_title	Single image dehazing based on multi-scale segmentation and deep learning Image dehazing (dpeaa)DE-He213 Multi-scale convolution (dpeaa)DE-He213 Multi-scale segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Atmospheric scattering model (dpeaa)DE-He213
topic	misc Image dehazing misc Multi-scale convolution misc Multi-scale segmentation misc Deep learning misc Atmospheric scattering model
topic_unstemmed	misc Image dehazing misc Multi-scale convolution misc Multi-scale segmentation misc Deep learning misc Atmospheric scattering model
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title	Single image dehazing based on multi-scale segmentation and deep learning
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title_full	Single image dehazing based on multi-scale segmentation and deep learning
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author_browse	Yu, Tianhe Zhu, Ming Chen, Haiming
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title_sort	single image dehazing based on multi-scale segmentation and deep learning
title_auth	Single image dehazing based on multi-scale segmentation and deep learning
abstract	Abstract Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
abstractGer	Abstract Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
abstract_unstemmed	Abstract Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
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container_issue	2
title_short	Single image dehazing based on multi-scale segmentation and deep learning
url	https://dx.doi.org/10.1007/s00138-022-01285-y
remote_bool	true
author2	Zhu, Ming Chen, Haiming
author2Str	Zhu, Ming Chen, Haiming
ppnlink	300186312
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doi_str	10.1007/s00138-022-01285-y
up_date	2024-07-03T21:41:47.834Z
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