dc.contributor.author |
Hilal, Ashfaaq |
|
dc.date.accessioned |
2024-04-05T09:57:11Z |
|
dc.date.available |
2024-04-05T09:57:11Z |
|
dc.date.issued |
2023 |
|
dc.identifier.citation |
Hilal, Ashfaaq (2023) Robust Single Image Dehazing and Image Enhancement via Generative Adversarial Networks. BSc. Dissertation, Informatics Institute of Technology |
en_US |
dc.identifier.issn |
2019394 |
|
dc.identifier.uri |
http://dlib.iit.ac.lk/xmlui/handle/123456789/1999 |
|
dc.description.abstract |
Haze removal is an essential factor where various research communities related to computer vision have conducted research with explicit outputs. Image de-hazing consumes more time, and the state-of-the-art solutions are less robust. In order to achieve this, a GAN-based approach is likely to be used, where de-hazing is performed unanimously and then retainment of the image is done accordingly. |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
Image de-hazing |
en_US |
dc.subject |
Generative Adversarial Network |
en_US |
dc.subject |
CCTV |
en_US |
dc.subject |
Image retainment |
en_US |
dc.title |
Robust Single Image Dehazing and Image Enhancement via Generative Adversarial Networks |
en_US |
dc.type |
Thesis |
en_US |