Dark Channel Prior Guided Conditional Generative Adversarial Network for Single Image Dehazing

摘要

Single image dehazing is a challenging while important problem as the existence of haze hinders most high-level computer vision tasks. Previous methods solve this problem using various hand-crafted priors or by CNN learning on synthetic data sets. In practice, many CNN based methods estimate the transmission maps and atmospheric lights without considering the predefined priors, and always need huge data to train the model. In this work, we propose Dark Channel Prior Guided Conditional Generative Adversarial Network, an end-to-end model that generates realistic haze-free images using hazy image input and dehaze image based on dark channel prior. A Siamese like encoder is proposed to extracted the feature, and multi-scale features are enhanced by feature aggregation block for decoding. Our algorithm can efficiently combine the prior-based and CNN based image dehazing method. Experimental results on synthetic datasets and real-world images demonstrate our model can generate more perceptually appealing dehazing results, and provide superior performance compared with the state-of-the-art methods.