HRN: Haze-Relevant Network Using Multi-Object Constraints for Single Image Dehazing

Abstract

In recent years, some deep learning dehazing methods based on atmospheric scattering model mostly solve the dehazing results by using depth convolution neural networks (CNNs) to estimate the medium transmission map in the model. However, these methods usually ignored the potential correlation between the transmission map and the atmospheric light in the atmospheric scattering model, which can lead to colour distortion and incomplete dehazing in the dehazing results. To address this problem, this paper first presents a novel Haze-Veil model to increase the correlation between the model parameters by constructing an atmospheric veil term. Then, based on the proposed model, a haze-relevant end-to-end network (HRN) is designed to estimate the parameters of this model and directly output the final clear image. In addition, a cost function is designed by defining multi-object constraint cost functions to further establish the connections between the statistical attributes of the hazy image and the out of HRN. Experiments on benchmark images, which include synthesized and real images, show that HRN effectively removes haze and outperforms most of the existing and state-of-the-art dehazing methods.