Journal article Open Access

Variance Reduction in Low Light Image Enhancement Model

V.deepika; C. Nivedha; P.S. Sai roshini; Guide: S. Arun Kumar


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  <identifier identifierType="URL">https://zenodo.org/record/5835282</identifier>
  <creators>
    <creator>
      <creatorName>V.deepika</creatorName>
      <affiliation>Computer science, SRM Institute of science and technology,  Chennai, India.</affiliation>
    </creator>
    <creator>
      <creatorName>C. Nivedha</creatorName>
      <affiliation>Computer science, SRM Institute of science and technology,  Chennai, India.</affiliation>
    </creator>
    <creator>
      <creatorName>P.S. Sai roshini</creatorName>
      <affiliation>Computer science, SRM Institute of science and technology,  Chennai, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Guide: S. Arun Kumar</creatorName>
      <affiliation>Assistant Professor Department of Computer  Science Engineering SRM Institute of Science &amp; Technology Chennai, India</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Variance Reduction in Low Light Image  Enhancement Model</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Image enhancement, Machine learning, Neural network, Pipeline.</subject>
    <subject subjectScheme="issn">2277-3878</subject>
    <subject subjectScheme="handle">100.1/ijrte.D4723119420</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  and Sciences Publication(BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2020-11-30</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5835282</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2277-3878</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijrte.D4723.119420</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In image processing, enhancement of images taken in low light is considered to be a tricky and intricate process, especially for the images captured at nighttime. It is because various factors of the image such as contrast, sharpness and color coordination should be handled simultaneously and effectively. To reduce the blurs or noises on the low-light images, many papers have contributed by proposing different techniques. One such technique addresses this problem using a pipeline neural network. Due to some irregularity in the working of the pipeline neural networks model [1], a hidden layer is added to the model which results in a decrease in irregularity.&lt;/p&gt;</description>
  </descriptions>
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