A novel CNN model for single image dehazing
Жипенг Ли, Cheng Liu, Yi Li, Xiaobing Zhong, Man Yuan, Xinwei Wan · 2024
Inspiring by the atmosphere shattering model, we deduced a novel transform model for image dehazing. Based on the prosed model we designed a light easy-training CNN network for end-to-end image dehazing tasks. We trained our module on RESIDE dataset and tested it on O-HAZY, I-HAZY and NH-HAZE datasets. The results suggest that comparing with AOD-Net and Dehaze-Net our proposed method gives a better performance in image dehazing. With the experiments, we also analyzed the mechanism of blue shifting and explained how our module would help in solving the blue shifting problem. As a light model, it can be combined with other detection network such as YOLO or F-RCNN easily to maintain complex tasks in future researches.