PHC-GAN: Physical Constraint Generative Adversarial Network for Single Image Dehazing
Gang Long, Wen Bing Lu, Lin Zha, Hongyi Zhang · 2020
Recently, most existing single image dehazing methods adopt the physical scattering model to generate clear images. The model variables are often estimated by trainable neural networks. However, estimating the variables heavily rely on dataset that usually does not take into account of the physical process induced by the scattering model. In this scheme, error accumulation cannot be avoided when applying the end-to-end methods. In this paper, we propose a physical constraint generative adversarial network (PHC-GAN) for single image dehazing. The PHC-GAN is a physics aware model that leveraging the physical scattering process as an additional constraint. To the best of our knowledge, we are the first introducing physical constraint in learning an end-to-end image dehazing model. Our proposed model not only effectively reduce the error accumulation, but can be well adapted in complex and realistic natural scenes compared to the existing methods. In detail, we realize the physical constraint in terms of a double discriminator architecture. The self-attention module is also utilized to guarantee fast convergence. In experiments, quantitative and qualitative results on both synthetic and natural images demonstrate that PHC-GAN is superior to state-of-the-art dehazing methods.