Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/118
Title: Literature Survey: Single Image Dehazing Using Deep Learning Techniques
Authors: Koranga, Pushpa
Singar, Sumitra
Sandeep, Gupta
Keywords: Atmospheric map
CNN
Deep learning
Single image dehazing
Issue Date: 2022
Publisher: IJFANS International Journal of Food and Nutritional Sciences
Series/Report no.: UGC CARE Listed ( Group -I);Journal Volume 11, S Iss 3,December 2022
Abstract: Bad 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.
Description: Literature Survey: Single Image Dehazing Using Deep Learning Techniques
URI: http://localhost:8080/xmlui/handle/123456789/118
ISSN: ISSN PRINT 2319 1775 Online 2320 7876
Appears in Collections:Faculty of Computing Skills Education

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