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dc.contributor.authorKoranga, Pushpa-
dc.contributor.authorSingar, Sumitra-
dc.contributor.authorSandeep, Gupta-
dc.date.accessioned2023-03-01T07:58:20Z-
dc.date.available2023-03-01T07:58:20Z-
dc.date.issued2022-
dc.identifier.issnISSN PRINT 2319 1775 Online 2320 7876-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/118-
dc.descriptionLiterature Survey: Single Image Dehazing Using Deep Learning Techniquesen_US
dc.description.abstractBad weather conditions, such as fog and haze, can significantly degrade the quality of a scene captured by a camera. Practically, this is due to the substantial presence of particles in the environment that absorb and scatter light. In computer vision, the absorption and scattering processes are commonly modeled by a linear combination of the direct attenuation and the airlight. To overcome such problem image dehazing techniques was adopted. In classic techniques dehazing was done by using some prior knowledge, but this technique gives color distortion, artifact effect etc in the output scene. In this paper we have discussed different types of Convolutional Neural Network techniques (CNN) which are based on training of dataset and overcome the problem of classic techniques.en_US
dc.language.isoenen_US
dc.publisherIJFANS International Journal of Food and Nutritional Sciencesen_US
dc.relation.ispartofseriesUGC CARE Listed ( Group -I);Journal Volume 11, S Iss 3,December 2022-
dc.subjectAtmospheric mapen_US
dc.subjectCNNen_US
dc.subjectDeep learningen_US
dc.subjectSingle image dehazingen_US
dc.titleLiterature Survey: Single Image Dehazing Using Deep Learning Techniquesen_US
dc.typeArticleen_US
Appears in Collections:Faculty of Computing Skills Education

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