Name
Abstract | ||
|---|---|---|
The contrast of remote sensing images captured in haze condition is poor, which influences their interpretation. In this letter, a novel dehazing algorithm based on the deformed haze imaging model is proposed. First, the model is deformed by introducing a translation term. Second, the atmospheric light and transmission are estimated according to the new model combined with dark channel prior. Lastly, the haze is successfully removed from remote sensing images using the proposed estimation algorithm. The estimated transmission is insensitive to the texture of ground objects, and the dehazing effect for nonuniform haze is more satisfactory than the compared method. Moreover, our approach can be used for general haze removal through adjusting the translation term. Experimental results reveal that the proposed method can recover the real scene clearly from haze remote sensing images along with the advantage of good color consistency. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/LSP.2015.2432466 | IEEE Signal Process. Lett. |
Keywords | Field | DocType |
color distortion,dark channel prior,haze removal,remote sensing | Computer vision,Remote sensing,Image based,Communication channel,Artificial intelligence,Mathematics,Haze | Journal |
Volume | Issue | ISSN |
22 | 10 | 1070-9908 |
Citations | PageRank | References |
12 | 0.56 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaochuan Pan | 1 | 215 | 79.94 |
| Fengying Xie | 2 | 182 | 15.33 |
| Zhenhua Jiang | 3 | 70 | 19.57 |
| Jingwei Yin | 4 | 17 | 9.05 |