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 pe...
