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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.2470-1173.2018.05.PMII-442</article-id>
      <article-id pub-id-type="sici">2470-1173(20180128)2018:5L.4421;1-</article-id>
      <article-id pub-id-type="publisher-id">s14.phd</article-id>
      <article-id pub-id-type="other">/ist/ei/2018/00002018/00000005/art00014</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>An Automatic Tuning Method for Camera Denoising and Sharpening based on a Perception Model</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Xi</surname>
            <given-names>Weijuan</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Zeng</surname>
            <given-names>Huan</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Phillips</surname>
            <given-names>Jonathan B.</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>28</day>
        <month>01</month>
        <year>2018</year>
      </pub-date>
      <volume>2018</volume>
      <issue>5</issue>
      <fpage>442-1</fpage>
      <lpage>442-7</lpage>
      <permissions>
        <copyright-year>2018</copyright-year>
      </permissions>
      <abstract>
        <p>Camera denoising and sharpening parameters are device related rigid parameters which are programmed in phone camera device. The current tuning method depends solely on manual modulation and visual evaluation of image quality, which is time consuming and difficult to optimally achieve.
 To this end, we will introduce an automatic tuning method for mobile cameras in this paper, which can tune the WNR parameters automatically and produce high quality images within a feasible processing time. The method contains two parts, a perception model and an optimization algorithm. For
 the first part, we developed a perception model to evaluate the image quality for mobile cameras through modified CPIQ metrics. For the second part, in order to overcome a high-dimension non-convex optimization problem, we developed a searching strategy to find the optimal solution by conducting
 quantization and iteratively minimizing the error metric of the perception model.</p>
      </abstract>
      <kwd-group>
        <kwd>CAMERA TUNING</kwd>
        <kwd>CPIQ</kwd>
        <kwd>IMAGE QUALITY</kwd>
        <kwd>DENOISE</kwd>
        <kwd>SHARPNESS</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
