Journal article Open Access
V.deepika; C. Nivedha; P.S. Sai roshini; Guide: S. Arun Kumar
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="041" ind1=" " ind2=" "> <subfield code="a">eng</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Image enhancement, Machine learning, Neural network, Pipeline.</subfield> </datafield> <controlfield tag="005">20220111134848.0</controlfield> <controlfield tag="001">5835282</controlfield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Computer science, SRM Institute of science and technology, Chennai, India.</subfield> <subfield code="a">C. Nivedha</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Computer science, SRM Institute of science and technology, Chennai, India.</subfield> <subfield code="a">P.S. Sai roshini</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Assistant Professor Department of Computer Science Engineering SRM Institute of Science & Technology Chennai, India</subfield> <subfield code="a">Guide: S. Arun Kumar</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Publisher</subfield> <subfield code="4">spn</subfield> <subfield code="a">Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">324575</subfield> <subfield code="z">md5:ff7f51abfe70f36e77abef115fcbf23a</subfield> <subfield code="u">https://zenodo.org/record/5835282/files/D4723119420.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2020-11-30</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire</subfield> <subfield code="o">oai:zenodo.org:5835282</subfield> </datafield> <datafield tag="909" ind1="C" ind2="4"> <subfield code="c">139-142</subfield> <subfield code="n">4</subfield> <subfield code="p">International Journal of Recent Technology and Engineering (IJRTE)</subfield> <subfield code="v">9</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Computer science, SRM Institute of science and technology, Chennai, India.</subfield> <subfield code="a">V.deepika</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Variance Reduction in Low Light Image Enhancement Model</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="650" ind1="1" ind2=" "> <subfield code="a">ISSN</subfield> <subfield code="0">(issn)2277-3878</subfield> </datafield> <datafield tag="650" ind1="1" ind2=" "> <subfield code="a">Retrieval Number</subfield> <subfield code="0">(handle)100.1/ijrte.D4723119420</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p>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.</p></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">issn</subfield> <subfield code="i">isCitedBy</subfield> <subfield code="a">2277-3878</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.35940/ijrte.D4723.119420</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">article</subfield> </datafield> </record>
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