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<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010604</journal-id>
      <journal-title>Electronic Imaging</journal-title>
      <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub"></issn>
      <publisher>
        <publisher-name>Society for Imaging Science and Technology</publisher-name>
        <publisher-loc>7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2020.16.AVM-081</article-id>
      <article-id pub-id-type="sici">2470-1173(20200126)2020:16L.811;1-</article-id>
      <article-id pub-id-type="publisher-id">ei_24701173_v2020n16_input/s10.xml</article-id>
      <article-id pub-id-type="other">/ist/ei/2020/00002020/00000016/art00009</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Single image haze removal using multiple scattering model for road scenes</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Kim</surname>
            <given-names>Minsub</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Hong</surname>
            <given-names>Soonyoung</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Kang</surname>
            <given-names>Moon Gi</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>26</day>
        <month>01</month>
        <year>2020</year>
      </pub-date>
      <volume>2020</volume>
      <issue>16</issue>
      <fpage>81-1</fpage>
      <lpage>81-6</lpage>
      <permissions>
        <copyright-year>2020</copyright-year>
      </permissions>
      <abstract>
        <p>
          <italic>Haze is one of the sources cause image degradation. Haze affects contrast and saturation of not only for the real world image, but also the road scenes. Most haze removal algorithms use an atmospheric scattering model for removing the effect of haze. Most of haze removal algorithms
 are based on the single scattering model which does not consider the blur in the haze image. In this paper, a novel haze removal algorithm using a multiple scattering model with deconvolution is proposed. The proposed algorithm considers blurring effect in the haze image. Down sampling of
 the haze image is also used for estimating the atmospheric light efficiently. The synthetic road scenes with and without haze are used to evaluate the performance of the proposed method. Experimental result demonstrates that the proposed algorithm performs better for restoring images affected
 by haze both qualitatively and quantitatively.</italic>
        </p>
      </abstract>
      <kwd-group>
        <kwd>Single image haze removal</kwd>
        <kwd>advanced driver assistance system</kwd>
        <kwd>atmospheric scattering model</kwd>
        <kwd>transmission map</kwd>
        <kwd>atmospheric point spread function</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
