| 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 |