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Guided-Pix2Pix+: End-to-end spatial and color refinement network for image dehazing
Inevitable hazy contamination degrades the visibility of images, and the resulting haze removal is one of the essential prerequisites for image processing and computer vision tasks. We proposed an end-to-end dehazing network, referred to as Guided-Pix2Pix, to estimate spatially refined transmission...
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Published in: | Signal processing. Image communication 2022-09, Vol.107, p.116758, Article 116758 |
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creator | Jiao, Libin Hu, Changmiao Huo, Lianzhi Tang, Ping |
description | Inevitable hazy contamination degrades the visibility of images, and the resulting haze removal is one of the essential prerequisites for image processing and computer vision tasks. We proposed an end-to-end dehazing network, referred to as Guided-Pix2Pix, to estimate spatially refined transmission maps and dehaze via the physical scattering equation. The remaining enhancement of color contrast, ill-posed adversarial training, and redundant backbone, however, should be thoroughly investigated, as required in the prospect of Guided-Pix2Pix. In this paper, we inherit the end-to-end structure of Guided-Pix2Pix and accordingly propose Guided-Pix2Pix+ as an update, which concatenates transmission estimation and refinement, physical scattering equation-based dehazing, together with color refinement, to achieve haze removal in a one-stage way. Specifically, we make use of the pretrained instance of EfficientNetB0 to estimate coarse-grained transmission maps and concatenate a guided filter layer to perform spatial refinement for the incoming transmission maps. Restored by the physical scattering equation, color refinement of dehazed proposals is finally performed via the standardization and clipping of pixel intensities. All the operations are differentiable, making it possible to achieve end-to-end, tight training. Furthermore, adversarial and perceptual losses are employed to regulate the performance of our model, giving rise to structurally similar but photo-realistic dehazed proposals. Extensive experiments confirm that our Guided-Pix2Pix+ yields dehazed proposals with fine-grained spatial refinement and relatively effective color contrast, compared to our previous Guided-Pix2Pix, the baseline, and advanced dehazing methods. The source code is currently available at https://github.com/92xianshen/guided-pix2pixplus.
[Display omitted]
•Guided-Pix2Pix+ for spatially and color-refined haze removal.•Stable compound adversarial training using WGAN-GP.•Lightweight but visually effective dehazing generator. |
doi_str_mv | 10.1016/j.image.2022.116758 |
format | article |
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[Display omitted]
•Guided-Pix2Pix+ for spatially and color-refined haze removal.•Stable compound adversarial training using WGAN-GP.•Lightweight but visually effective dehazing generator.</description><identifier>ISSN: 0923-5965</identifier><identifier>EISSN: 1879-2677</identifier><identifier>DOI: 10.1016/j.image.2022.116758</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Color refinement ; End-to-end image dehazing ; Intensity standardization and outlier-clipping ; Spatial transmission refinement</subject><ispartof>Signal processing. Image communication, 2022-09, Vol.107, p.116758, Article 116758</ispartof><rights>2022 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c303t-a9090821eb74238ac5464c0ecf01ce42abf009b752c331d9fc0695509e0257103</citedby><cites>FETCH-LOGICAL-c303t-a9090821eb74238ac5464c0ecf01ce42abf009b752c331d9fc0695509e0257103</cites><orcidid>0000-0001-5285-652X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><creatorcontrib>Jiao, Libin</creatorcontrib><creatorcontrib>Hu, Changmiao</creatorcontrib><creatorcontrib>Huo, Lianzhi</creatorcontrib><creatorcontrib>Tang, Ping</creatorcontrib><title>Guided-Pix2Pix+: End-to-end spatial and color refinement network for image dehazing</title><title>Signal processing. Image communication</title><description>Inevitable hazy contamination degrades the visibility of images, and the resulting haze removal is one of the essential prerequisites for image processing and computer vision tasks. We proposed an end-to-end dehazing network, referred to as Guided-Pix2Pix, to estimate spatially refined transmission maps and dehaze via the physical scattering equation. The remaining enhancement of color contrast, ill-posed adversarial training, and redundant backbone, however, should be thoroughly investigated, as required in the prospect of Guided-Pix2Pix. In this paper, we inherit the end-to-end structure of Guided-Pix2Pix and accordingly propose Guided-Pix2Pix+ as an update, which concatenates transmission estimation and refinement, physical scattering equation-based dehazing, together with color refinement, to achieve haze removal in a one-stage way. Specifically, we make use of the pretrained instance of EfficientNetB0 to estimate coarse-grained transmission maps and concatenate a guided filter layer to perform spatial refinement for the incoming transmission maps. Restored by the physical scattering equation, color refinement of dehazed proposals is finally performed via the standardization and clipping of pixel intensities. All the operations are differentiable, making it possible to achieve end-to-end, tight training. Furthermore, adversarial and perceptual losses are employed to regulate the performance of our model, giving rise to structurally similar but photo-realistic dehazed proposals. Extensive experiments confirm that our Guided-Pix2Pix+ yields dehazed proposals with fine-grained spatial refinement and relatively effective color contrast, compared to our previous Guided-Pix2Pix, the baseline, and advanced dehazing methods. The source code is currently available at https://github.com/92xianshen/guided-pix2pixplus.
[Display omitted]
•Guided-Pix2Pix+ for spatially and color-refined haze removal.•Stable compound adversarial training using WGAN-GP.•Lightweight but visually effective dehazing generator.</description><subject>Color refinement</subject><subject>End-to-end image dehazing</subject><subject>Intensity standardization and outlier-clipping</subject><subject>Spatial transmission refinement</subject><issn>0923-5965</issn><issn>1879-2677</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKu_wEvuknWSbHY3ggcptQoFBfUc0mS2pm53S3b9_PWmrWcPwwwD78zDQ8g5h4wDLy5XWVjbJWYChMg4L0pVHZARr0rNRFGWh2QEWkimdKGOyUnfrwBA5KBH5Gn2Hjx69hi-RKqLKzptPRs6hq2n_cYOwTbUptl1TRdpxDq0uMZ2oC0On118o3Va775Tj6_2J7TLU3JU26bHs78-Ji-30-fJHZs_zO4nN3PmJMiBWQ0aKsFxUeZCVtapvMgdoKuBO8yFXdQAelEq4aTkXtcOCq0UaAShSg5yTOT-rotd3yc0s4mJJH4bDmbrxazMjsxsvZi9l5S63qcwoX0EjKZ3AVuHPkR0g_Fd-Df_C44_a7U</recordid><startdate>202209</startdate><enddate>202209</enddate><creator>Jiao, Libin</creator><creator>Hu, Changmiao</creator><creator>Huo, Lianzhi</creator><creator>Tang, Ping</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-5285-652X</orcidid></search><sort><creationdate>202209</creationdate><title>Guided-Pix2Pix+: End-to-end spatial and color refinement network for image dehazing</title><author>Jiao, Libin ; Hu, Changmiao ; Huo, Lianzhi ; Tang, Ping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c303t-a9090821eb74238ac5464c0ecf01ce42abf009b752c331d9fc0695509e0257103</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Color refinement</topic><topic>End-to-end image dehazing</topic><topic>Intensity standardization and outlier-clipping</topic><topic>Spatial transmission refinement</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiao, Libin</creatorcontrib><creatorcontrib>Hu, Changmiao</creatorcontrib><creatorcontrib>Huo, Lianzhi</creatorcontrib><creatorcontrib>Tang, Ping</creatorcontrib><collection>CrossRef</collection><jtitle>Signal processing. Image communication</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiao, Libin</au><au>Hu, Changmiao</au><au>Huo, Lianzhi</au><au>Tang, Ping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Guided-Pix2Pix+: End-to-end spatial and color refinement network for image dehazing</atitle><jtitle>Signal processing. Image communication</jtitle><date>2022-09</date><risdate>2022</risdate><volume>107</volume><spage>116758</spage><pages>116758-</pages><artnum>116758</artnum><issn>0923-5965</issn><eissn>1879-2677</eissn><abstract>Inevitable hazy contamination degrades the visibility of images, and the resulting haze removal is one of the essential prerequisites for image processing and computer vision tasks. We proposed an end-to-end dehazing network, referred to as Guided-Pix2Pix, to estimate spatially refined transmission maps and dehaze via the physical scattering equation. The remaining enhancement of color contrast, ill-posed adversarial training, and redundant backbone, however, should be thoroughly investigated, as required in the prospect of Guided-Pix2Pix. In this paper, we inherit the end-to-end structure of Guided-Pix2Pix and accordingly propose Guided-Pix2Pix+ as an update, which concatenates transmission estimation and refinement, physical scattering equation-based dehazing, together with color refinement, to achieve haze removal in a one-stage way. Specifically, we make use of the pretrained instance of EfficientNetB0 to estimate coarse-grained transmission maps and concatenate a guided filter layer to perform spatial refinement for the incoming transmission maps. Restored by the physical scattering equation, color refinement of dehazed proposals is finally performed via the standardization and clipping of pixel intensities. All the operations are differentiable, making it possible to achieve end-to-end, tight training. Furthermore, adversarial and perceptual losses are employed to regulate the performance of our model, giving rise to structurally similar but photo-realistic dehazed proposals. Extensive experiments confirm that our Guided-Pix2Pix+ yields dehazed proposals with fine-grained spatial refinement and relatively effective color contrast, compared to our previous Guided-Pix2Pix, the baseline, and advanced dehazing methods. The source code is currently available at https://github.com/92xianshen/guided-pix2pixplus.
[Display omitted]
•Guided-Pix2Pix+ for spatially and color-refined haze removal.•Stable compound adversarial training using WGAN-GP.•Lightweight but visually effective dehazing generator.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.image.2022.116758</doi><orcidid>https://orcid.org/0000-0001-5285-652X</orcidid></addata></record> |
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subjects | Color refinement End-to-end image dehazing Intensity standardization and outlier-clipping Spatial transmission refinement |
title | Guided-Pix2Pix+: End-to-end spatial and color refinement network for image dehazing |
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