Universal Dehazing via Haze Style Transfer

Single image dehazing has been actively studied to overcome the quality degradation of hazy images. Most of the existing methods take model-based approaches and the existing learning-based methods usually target specific haze styles only, e.g., daytime, varicolored, and nighttime haze. Therefore, they suffer from the limited performance on arbitrary hazy images with diverse characteristics due to the lack of universal training dataset. In this paper, we first propose a fully data-driven learning-based framework for universal dehazing based on the haze style transfer (HST). We define multiple domains of haze styles by applying the K-means clustering to the background light of diverse real hazy images. We design the haze style modulator to extract the scene radiance features and the haze-related features, respectively. We employ the unpaired image-to-image translation methodology to transfer a source hazy image into different hazy images with diverse styles while preserving the scene radiance. The generated diverse hazy images are used to train the universal dehazing network in a semi-supervised manner, where we implement the dehazing as a special instance of HST into no haze style. The experimental results show that the proposed framework reliably generates realistic and diverse hazy images, and achieves better performance of universal dehazing regardless of the haze styles compared with the existing state-of-the art dehazing methods.