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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 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 0058 IJFANS 2022.pdf | Literature Survey: Single Image Dehazing Using Deep Learning Techniques | 660.02 kB | Adobe PDF | ![]() View/Open |
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