An Image dehazing approach based on the airlight field estimation
This addresses image quality issues in computer vision applications like photography or surveillance, but it is incremental as it builds on existing physical models.
The paper tackles the problem of single image haze removal by modeling atmospheric light as a field function instead of a constant, which reduces brightness imbalance and color distortion in recovered images, especially for images with large sky regions. Evaluation shows it outperforms existing methods in dehazing.
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.