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Fog and haze are weathers with low visibility which are adversarial to thedriving safety of intelligent vehicles equipped with optical sensors likecameras and LiDARs. Therefore image dehazing for perception enhancement andhaze image synthesis for testing perception abilities are equivalentlyimportant in the development of such autonomous driving systems. From the viewof image translation, these two problems are essentially dual with each other,which have the potentiality to be solved jointly. In this paper, we propose anunsupervised Image-to-Image Translation framework based on VariationalAutoencoders (VAE) and Generative Adversarial Nets (GAN) to handle haze imagesynthesis and haze removal simultaneously. Since the KL divergence in the VAEobjectives could not guarantee the optimal mapping under imbalanced andunpaired training samples with limited size, Maximum mean discrepancy (MMD)based VAE is utilized to ensure the translating consistency in both directions.The comprehensive analysis on both synthesis and dehazing performance of ourmethod demonstrate the feasibility and practicability of the proposed method.

Joint haze image synthesis and dehazing with mmd-vae losses