A 3D Attenuation Coefficient based Degradation Estimation for Real Non-Homogeneous Dehazing

The end-to-end image dehazing network relies on paired training data. However, there is limited training data available for real non-homogeneous dehazing, which limits the performance of dehazing networks on real non-homogeneous hazy images. To overcome this limitation, we propose a method to augment the training datasets for real non-homogeneous image dehazing. Different with existing methods that introduce augment data from other datasets, we estimate the degradations and synthesize additional hazy images by applying it to other scenes in the same dataset. During the estimation, we proposed a new degradation model based on 3D attenuation coefficient for describing non-homogeneous degradation. To solve the 3D attenuation coefficient, we propose a clue kernel to overcome the scene-dependence of the estimated degradation. The experimental results show that our proposed method can effectively augment the training dataset and improve the performance and robustness of the dehazing network. Compared to state-of-the-art methods, our approach outperforms the best performing method by 4.65% SSIM on the Dense-NH-HAZE dataset.