A Method of Image Dehazing Based on Atmospheric Veil Prediction by ResNet

Jie Zhang, Fan Li, Mengfei Kang, Xiongbiao Luo, Jing Zhao, Chuan Xiao, Haipeng Du, Huaijun Wang

Abstract: Image defogging is an important prerequisite for video summary. In existing defogging methods, there are some weaknesses such as too long parameters calculation time such as transmission map and atmospheric veil estimation. We propose a new method independent of the transmission map, a deep ResNet are trained to learn and predict the veil. There is no need to estimate more parameters for the defogging algorithm, and thus the quality of scene radiation recovery is improved, meanwhile parameter calculations training is speed up. From the experimental results on the dataset, we can see that the training time required for parameter calculation is reduced greatly, while the performance of image defogging is improved by proposed method, meanwhile, the distortion of depth integrity is minimal.