Two-Layer Gaussian Process Regression With Example Selection for Image Dehazing
Researchers have devoted great efforts to image dehazing with prior assumptions in the past decade. Recently developed example-based approaches typically lack elegant models for the hazy process and meanwhile demand synthetic hazy images by manual selection. The priors from observations, and those t...
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Veröffentlicht in: | IEEE transactions on circuits and systems for video technology 2017-12, Vol.27 (12), p.2505-2517 |
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creator | Fan, Xin Wang, Yi Tang, Xianxuan Gao, Renjie Luo, Zhongxuan |
description | Researchers have devoted great efforts to image dehazing with prior assumptions in the past decade. Recently developed example-based approaches typically lack elegant models for the hazy process and meanwhile demand synthetic hazy images by manual selection. The priors from observations, and those trained from synthetic images cannot always reflect true structural information of natural images in practice. In this paper, we present a learning model for haze removal by using two-layer Gaussian process regression (GPR). By using training examples, the two-layer GPR establishes a direct relationship from the input image to the depth-dependent transmission, and learns local image priors to further improve the estimation. We also provide a systematic scheme to automatically collect suitable training pairs, which works for both simulated examples and images of natural scenes. Both qualitative and quantitative comparisons on real-world and synthetic hazy images demonstrate the effectiveness of the proposed approach, especially for white or bright objects and heavy haze regions in which traditional methods may fail. |
doi_str_mv | 10.1109/TCSVT.2016.2592328 |
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Recently developed example-based approaches typically lack elegant models for the hazy process and meanwhile demand synthetic hazy images by manual selection. The priors from observations, and those trained from synthetic images cannot always reflect true structural information of natural images in practice. In this paper, we present a learning model for haze removal by using two-layer Gaussian process regression (GPR). By using training examples, the two-layer GPR establishes a direct relationship from the input image to the depth-dependent transmission, and learns local image priors to further improve the estimation. We also provide a systematic scheme to automatically collect suitable training pairs, which works for both simulated examples and images of natural scenes. 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(IEEE) 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-b0d2f261b5a6b3704944c5ca260132c9ab33978fd3d32173b340d2bcb57686573</citedby><cites>FETCH-LOGICAL-c295t-b0d2f261b5a6b3704944c5ca260132c9ab33978fd3d32173b340d2bcb57686573</cites><orcidid>0000-0002-8991-4188</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7514954$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7514954$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Fan, Xin</creatorcontrib><creatorcontrib>Wang, Yi</creatorcontrib><creatorcontrib>Tang, Xianxuan</creatorcontrib><creatorcontrib>Gao, Renjie</creatorcontrib><creatorcontrib>Luo, Zhongxuan</creatorcontrib><title>Two-Layer Gaussian Process Regression With Example Selection for Image Dehazing</title><title>IEEE transactions on circuits and systems for video technology</title><addtitle>TCSVT</addtitle><description>Researchers have devoted great efforts to image dehazing with prior assumptions in the past decade. Recently developed example-based approaches typically lack elegant models for the hazy process and meanwhile demand synthetic hazy images by manual selection. The priors from observations, and those trained from synthetic images cannot always reflect true structural information of natural images in practice. In this paper, we present a learning model for haze removal by using two-layer Gaussian process regression (GPR). By using training examples, the two-layer GPR establishes a direct relationship from the input image to the depth-dependent transmission, and learns local image priors to further improve the estimation. We also provide a systematic scheme to automatically collect suitable training pairs, which works for both simulated examples and images of natural scenes. Both qualitative and quantitative comparisons on real-world and synthetic hazy images demonstrate the effectiveness of the proposed approach, especially for white or bright objects and heavy haze regions in which traditional methods may fail.</description><subject>Computational modeling</subject><subject>Computer simulation</subject><subject>Estimation</subject><subject>Example selection</subject><subject>Gaussian process</subject><subject>Gaussian process regression (GPR)</subject><subject>Gaussian processes</subject><subject>Ground penetrating radar</subject><subject>Haze</subject><subject>image dehazing</subject><subject>Image resolution</subject><subject>Image restoration</subject><subject>Image transmission</subject><subject>Training</subject><issn>1051-8215</issn><issn>1558-2205</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMFOAjEQhhujiYi-gF6aeF5sp-22PRoEJCHByKrHplu6sAR2sV2i-PQuQjz9k8n_zSQfQreU9Cgl-iHrz96zHhCa9kBoYKDOUIcKoRIAIs7bmQiaKKDiEl3FuCKEcsVlB02zrzqZ2L0PeGR3MZa2wi-hdj5G_OoXoc2yrvBH2Szx4NtutmuPZ37tXXNYF3XA441dePzkl_anrBbX6KKw6-hvTtlFb8NB1n9OJtPRuP84SRxo0SQ5mUMBKc2FTXMmCdecO-EspIQycNrmjGmpijmbM6CS5Yy3RO5yIVOVCsm66P54dxvqz52PjVnVu1C1Lw3VUoJkKai2BceWC3WMwRdmG8qNDXtDiTmIM3_izEGcOYlrobsjVHrv_wEpKNeCs1_JfWiv</recordid><startdate>20171201</startdate><enddate>20171201</enddate><creator>Fan, Xin</creator><creator>Wang, Yi</creator><creator>Tang, Xianxuan</creator><creator>Gao, Renjie</creator><creator>Luo, Zhongxuan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-8991-4188</orcidid></search><sort><creationdate>20171201</creationdate><title>Two-Layer Gaussian Process Regression With Example Selection for Image Dehazing</title><author>Fan, Xin ; Wang, Yi ; Tang, Xianxuan ; Gao, Renjie ; Luo, Zhongxuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-b0d2f261b5a6b3704944c5ca260132c9ab33978fd3d32173b340d2bcb57686573</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Computational modeling</topic><topic>Computer simulation</topic><topic>Estimation</topic><topic>Example selection</topic><topic>Gaussian process</topic><topic>Gaussian process regression (GPR)</topic><topic>Gaussian processes</topic><topic>Ground penetrating radar</topic><topic>Haze</topic><topic>image dehazing</topic><topic>Image resolution</topic><topic>Image restoration</topic><topic>Image transmission</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fan, Xin</creatorcontrib><creatorcontrib>Wang, Yi</creatorcontrib><creatorcontrib>Tang, Xianxuan</creatorcontrib><creatorcontrib>Gao, Renjie</creatorcontrib><creatorcontrib>Luo, Zhongxuan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on circuits and systems for video technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fan, Xin</au><au>Wang, Yi</au><au>Tang, Xianxuan</au><au>Gao, Renjie</au><au>Luo, Zhongxuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Two-Layer Gaussian Process Regression With Example Selection for Image Dehazing</atitle><jtitle>IEEE transactions on circuits and systems for video technology</jtitle><stitle>TCSVT</stitle><date>2017-12-01</date><risdate>2017</risdate><volume>27</volume><issue>12</issue><spage>2505</spage><epage>2517</epage><pages>2505-2517</pages><issn>1051-8215</issn><eissn>1558-2205</eissn><coden>ITCTEM</coden><abstract>Researchers have devoted great efforts to image dehazing with prior assumptions in the past decade. Recently developed example-based approaches typically lack elegant models for the hazy process and meanwhile demand synthetic hazy images by manual selection. The priors from observations, and those trained from synthetic images cannot always reflect true structural information of natural images in practice. In this paper, we present a learning model for haze removal by using two-layer Gaussian process regression (GPR). By using training examples, the two-layer GPR establishes a direct relationship from the input image to the depth-dependent transmission, and learns local image priors to further improve the estimation. We also provide a systematic scheme to automatically collect suitable training pairs, which works for both simulated examples and images of natural scenes. Both qualitative and quantitative comparisons on real-world and synthetic hazy images demonstrate the effectiveness of the proposed approach, especially for white or bright objects and heavy haze regions in which traditional methods may fail.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TCSVT.2016.2592328</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-8991-4188</orcidid></addata></record> |
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subjects | Computational modeling Computer simulation Estimation Example selection Gaussian process Gaussian process regression (GPR) Gaussian processes Ground penetrating radar Haze image dehazing Image resolution Image restoration Image transmission Training |
title | Two-Layer Gaussian Process Regression With Example Selection for Image Dehazing |
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