Abstract
In this paper, we propose a simple but powerful prior, color attenuation prior, for
haze removal from a single input hazy image. By creating a linear model for modelling the scene depth of the hazy image under this novel prior and learning the parameters of the model by using a supervised learning method, the depth
information can be well recovered. With the depth map of the hazy image, we can
easily remove haze from a single image. Experimental results show that the proposed approach is highly efficient and it outperforms state-of-the-art haze removal algorithms in terms of the dehazing effect as well.
haze removal from a single input hazy image. By creating a linear model for modelling the scene depth of the hazy image under this novel prior and learning the parameters of the model by using a supervised learning method, the depth
information can be well recovered. With the depth map of the hazy image, we can
easily remove haze from a single image. Experimental results show that the proposed approach is highly efficient and it outperforms state-of-the-art haze removal algorithms in terms of the dehazing effect as well.
| Original language | English |
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| Publication status | Published - 2014 |
| Event | 25th British Machine Vision Conference, BMVC 2014 - Nottingham, United Kingdom Duration: 1 Sep 2014 → 5 Sep 2014 |
Conference
| Conference | 25th British Machine Vision Conference, BMVC 2014 |
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| Country/Territory | United Kingdom |
| City | Nottingham |
| Period | 1/09/14 → 5/09/14 |