Z. Li, C. Zheng, H. Shu and S. Wu, & Single Image Dehazing via Model-Based Deep-Learning, 2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 141-145, doi: 10.1109/ICIP46576.2022.9897479.
Model-based single image dehazing algorithms restore images with sharp edges and rich details at the expense of low PSNR values. Data-driven ones restore images with high PSNR values but with low contrast, and even some remaining haze. In this paper, a novel single image dehazing algorithm is introduced by integrating model-based and data-driven approaches. Both transmission map and atmospheric light are initialized by the model-based methods, and refined by deep learning based approaches which form a neural augmentation. Haze-free images are restored by using the transmission map and atmospheric light. Experimental results indicate that the proposed algorithm can remove haze well from real-world and synthetic hazy images.
There was no specific funding for the research done