Towards simulating foggy and hazy images and evaluating their authenticity

To train and evaluate fog/haze removal models, it is highly desired but burdensome to collect a large-scale dataset comprising well-aligned foggy/hazy images with their fog-free/haze-free versions. In this paper, we propose a framework, namely Foggy and Hazy Images Simulator (FoHIS for short), to simulate more realistic fog and haze effects at any elevation in images. What’s more, no former studies have introduced objective methods to evaluate the authenticity of synthetic foggy/hazy images. We innovatively design an Authenticity Evaluator for Synthetic foggy/hazy Images (AuthESI for short) to objectively measure which simulation algorithm could achieve more natural-looking results. We compare FoHIS with another two state-of-the-art methods, and the subjective results show that it outperforms those competitors. Besides, the prediction on simulated image’s authenticity made by AuthESI is highly consistent with subjective judgements (Source codes are publicly available at https://github.com/noahzn/FoHIS).