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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>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2016.13.IQSP-225</article-id>
      <article-id pub-id-type="sici">2470-1173(20160214)2016:13L.1;1-</article-id>
      <article-id pub-id-type="publisher-id">s29.phd</article-id>
      <article-id pub-id-type="other">/ist/ei/2016/00002016/00000013/art00029</article-id>
      <article-categories>
        <subj-group>
          <subject>Image Quality and System Performance XIII Interactive Papers Session</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Noise-Free Rule-Based Fuzzy Image Enhancement</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Roopaei</surname>
            <given-names>Mehdi</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Eghbal</surname>
            <given-names>Morad Khosravi</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Shadaram</surname>
            <given-names>Mehdi</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Agaian</surname>
            <given-names>Sos</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>14</day>
        <month>02</month>
        <year>2016</year>
      </pub-date>
      <volume>2016</volume>
      <issue>13</issue>
      <fpage>1</fpage>
      <lpage>5</lpage>
      <permissions>
        <copyright-year>2016</copyright-year>
      </permissions>
      <abstract>
        <p>Different kinds of noises have considerable effects on most of image sensing systems. Suitable image contrast enhancement algorithms can improve contrast or retain detail information while reducing noises as well. Fuzzy representation of an image provides a reliable analysis when inexactness
 occurred at the gray level values. This paper presents a fuzzy-based novel image contrast enhancement method. Several image quality indices, such as similarity, naturalness, and mean brightness preserving examined and experimentally show the effectiveness of the proposed technique in comparison
 with well-known image enhancement methods such as histogram equalization and contrast limited adaptive histogram equalization methods.</p>
      </abstract>
      <kwd-group>
        <kwd>FUZZY SYSTEM</kwd>
        <kwd>IMAGE ENHANCEMENT</kwd>
        <kwd>IMAGE ROBUSTNESS AND NATURALNESS</kwd>
        <kwd>IMAGE QUALITY INDEX</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
