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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  <dc:contributor>Blue Eyes Intelligence Engineering  and Sciences Publication(BEIESP)</dc:contributor>
  <dc:creator>V.deepika</dc:creator>
  <dc:creator>C. Nivedha</dc:creator>
  <dc:creator>P.S. Sai roshini</dc:creator>
  <dc:creator>Guide: S. Arun Kumar</dc:creator>
  <dc:date>2020-11-30</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://zenodo.org/record/5835282</dc:identifier>
  <dc:identifier>10.35940/ijrte.D4723.119420</dc:identifier>
  <dc:identifier>oai:zenodo.org:5835282</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>issn:2277-3878</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:source>International Journal of Recent Technology and Engineering (IJRTE) 9(4) 139-142</dc:source>
  <dc:subject>Image enhancement, Machine learning, Neural network, Pipeline.</dc:subject>
  <dc:subject>ISSN</dc:subject>
  <dc:subject>Retrieval Number</dc:subject>
  <dc:title>Variance Reduction in Low Light Image  Enhancement Model</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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