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From depth-aware haze generation to real-world haze removal

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Abstract

For deep learning-based single image dehazing works, their performances seriously depend on the designed models and training dataset. Existing state-of-the-art methods focus on the design of novel dehazing models or the improvement of training strategies to obtain better dehazing results. In this work, instead of designing a new deep dehazing model, we attempt to further improve the dehazing performance from the perspective of enriching training datasets by exploring an intuitive yet efficient way to synthesize photo-realistic hazy images. It is well known that for a natural hazy image, its perceived haze density increases with scene depth. Motivated by this, we develop a depth-aware haze generation network, namely HazeGAN, by incorporating the Generative Adversarial Network (GAN), depth estimation network, and physical atmospheric scattering to progressively synthesize hazy images. Specifically, a separate depth estimation network is embedded to obtain multi-scale depth features, which are exploited by the atmospheric scattering model to generate multi-scale hazy features. The hazy features are fused into the GAN generator to output synthetic hazy images with depth-aware haze effects. Extensive experimental results demonstrate that the proposed HazeGAN can generate diverse training pairs of depth-aware hazy images and clear images, which effectively enrich the existing benchmark datasets, and improve the generalization capabilities of existing deep image dehazing models.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the first author or the corresponding author on reasonable and motivated request.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (61972143,62172059), and in part by the Scientific Research Fund of Hunan Provincial Education Department (21C0534), and Scientific Research Fund of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application (2021HSKFJJ040).

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Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Jiyou Chen, Gaobo Yang & Ming Xia

  2. School of physics and Electronics Engineering, Hengyang Normal University, Hengyang, 421008, China

    Jiyou Chen

  3. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Dengyong Zhang

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Chen, J., Yang, G., Xia, M. et al. From depth-aware haze generation to real-world haze removal. Neural Comput & Applic 35, 8281–8293 (2023). https://doi.org/10.1007/s00521-022-08101-8

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