Name
Abstract | ||
|---|---|---|
Defogging is an important image enhancement and restoration technique that is widely used for various computer vision and computational photography applications. While the vast majority of currently available defogging methods work well for daytime foggy images, they generally remain challenging to dehaze nighttime hazy images. This work proposes a new visibility-guided fusion framework to defog nighttime images. We first use fast visibility recovery to restore the hazy image. On the other hand, we enhance the foggy image to improve its contrast. Finally, an illumination fusion step is performed to precisely remove fog. The experimental results demonstrate that our proposed method is effective to remove fog or haze on nighttime images. In particular, it provides an efficient strategy to defog nighttime foggy images. |
Year | Venue | Field |
|---|---|---|
2018 | PRCV | Computer vision,Visibility,Image fusion,Computer science,Computational photography,Fusion,Artificial intelligence,Haze |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiongbiao Luo | 1 | 124 | 22.22 |
| Yingying Guo | 2 | 0 | 1.01 |
| Henry Chidozie Ewurum | 3 | 0 | 0.34 |
| Zhao Feng | 4 | 2 | 1.83 |
| Jie Yang | 5 | 282 | 57.59 |