Towards real-world haze removal with uncorrelated graph model

Xiaozhe Meng, Yuxin Feng, Fan Zhou, Yun Liang, Zhuo Su

Abstract: The scene restoration effect of the dehazing algorithms usually suffers from double quality interference. First, the dehazing methods based on the atmospheric scattering model (ASM) lead to light loss. Second, the synthetic data used for supervised learning may produce apparent data deviation from the real hazy scene, which seriously weakens the generalization ability of the model. To address the above problems, we propose an uncorrelated graph dehazing model for real-world scenes. The process firstly eliminates the correlation in the representation space by establishing a directed acyclic graph with isolated points, thus enhancing the generalization performance of the model. Secondly, to suppress over-exposure that may occur on real-world data, the proposed model training is performed with a combination of light control. This work improves the ASM and constructs a new dataset applicable to natural haze scenes. Finally, the effectiveness of the proposed method can be verified through multiple experimental comparisons.