Unsupervised single-image dehazing using the multiple-scattering model

Abstract

An unsupervised single-image dehazing method using a multiple scattering model is proposed. The method uses an undegraded atmospheric multiple scattering model and unsupervised learning to implement dehazing on single real-world image. The atmospheric multiple scattering model can avoid the influence of multiple scattering on the image and the unsupervised neural network can avoid the intensive operation on the data set. In this method, three unsupervised learning branches and a blur kernel estimation module estimate the scene radiation layer, transmission layer, atmospheric light layer, and blur kernel layer, respectively. In addition, the unsupervised loss function is constructed by prior knowledge to constrain the unsupervised branches. Finally, the output of the three unsupervised branches and the blur kernel estimation module synthesizes the haze image in a self-supervised way. A large number of experiments show that the proposed method has good performance in image dehazing compared with the six most advanced dehazing methods.