Name
Abstract | ||
|---|---|---|
This paper proposes a scheme for single image haze removal based on the airlight field (ALF) estimation. Conventional image dehazing methods which are based on a physical model generally take the global atmospheric light as a constant. However, the constant-airlight assumption may be unsuitable for images with large sky regions, which causes unacceptable brightness imbalance and color distortion in recovery images. This paper models the atmospheric light as a field function, and presents a maximum a-priori (MAP) method for jointly estimating the airlight field, the transmission rate and the haze free image. We also introduce a valid haze-level prior for effective estimate of transmission. Evaluation on real world images shows that the proposed approach outperforms existing methods in single image dehazing, especially when the large sky region is included. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Transmission rate,Pattern recognition,Computer science,Sky,Artificial intelligence,Distortion,Brightness,Haze |
DocType | Volume | Citations |
Journal | abs/1805.02142 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lijun Zhang | 1 | 245 | 37.10 |
| Yongbin Gao | 2 | 0 | 0.68 |
| Yujin Zhang | 3 | 0 | 1.69 |