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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/J.ImagingSci.Technol.2019.63.6.060411</article-id>
      <article-id pub-id-type="sici">2470-1173(20200126)2020:10L.604111;1-</article-id>
      <article-id pub-id-type="publisher-id">ei_24701173_v2020n10_input/s17.xml</article-id>
      <article-id pub-id-type="other">/ist/ei/2020/00002020/00000010/art00025</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Fractional Contrast Stretching for Image Enhancement of Aerial and Satellite Images</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Trongtirakul</surname>
            <given-names>Thaweesak</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Chiracharit</surname>
            <given-names>Werapon</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Imberman</surname>
            <given-names>Susan</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Agaian</surname>
            <given-names>Sos</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>26</day>
        <month>01</month>
        <year>2020</year>
      </pub-date>
      <volume>2020</volume>
      <issue>10</issue>
      <fpage>60411-1</fpage>
      <lpage>60411-11</lpage>
      <permissions>
        <copyright-year>2020</copyright-year>
      </permissions>
      <abstract>
        <p>
          <italic>Aerial and satellite photographs suffer from uncontrollable weather conditions. Frequently, illumination of the same region can be totally different. This is usually due to shadowing self-obstruction or light reflection. Existing image enhancement methods fail to improve hidden details
 and local contrast at the same visualization level. They are not developed to enhance through local dark or light regions simultaneously. Also, the current aerial and satellite image enhancement methods have several limitations. For instance, these include intensity saturation, non-uniform
 brightness, halo effect, blur edges, and so on. This article introduces a fractional contrast stretching concept for aerial and satellite image enhancement based on a novel automated non-uniform luminance normalization that is not provided by the user as input parameters. The introduced approach
 contains several new techniques: (i) no reference non-linearly fractional contrast stretching with automatic non-uniform luminance normalization and (ii) non-linearly local contrast stretching for spatial details and edge sharpening. The proposed algorithm was tested on the orthorectified
 aerial photograph database with a pixel resolution of 1 meter or finer from across the United States during 2000–2016. The simulation results illustrate the efficiency of the proposed algorithm and its advantages for cutting-edge aerial and satellite image enhancement, resulting in visualization
 quality.</italic>
        </p>
      </abstract>
      <kwd-group>
        <kwd>fractional non-linear contrast stretching</kwd>
        <kwd>local non-linear contrast stretching</kwd>
        <kwd>satellite image enhancement</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
