Single Image Haze Removal via Joint Estimation of Detail and Transmission
Shengdong Zhang, Jian Yao, Wenqi Ren · 2018
Single image haze removal is an important task in computer vision. However, haze removal is an extremely challenging problem due to it is massively ill-posed, which is that we need to estimate the transmission and the corresponding haze-free pixel from a single color measurement at each pixel. In this paper, we propose a new deep learning based method for removing haze from single input image. First, we estimate a transmission map via joint estimation of clear image details and transmission map, which is different from traditional methods which only estimating a transmission map for a hazy image. Second, we use a global regularization method to eliminate the halos and artifacts. Experimental results demonstrate that our method outperforms the other state-of-the-art dehazing methods.