Single Image Dehazing of Multiscale Deep-Learning Based on Dual-Domain Decomposition

The traditional single image dehazing algorithms arc susceptible to the prior information of hazy images, resulting in color distortion. Furthermore, the deep-learning dehazing algorithms arc limited by the network model, leading to residual haze. To overcome these problems, this study proposes a single image dehazing method of multiscale deep-learning based on dual-domain decomposition. This method develops a multiscale deep-learning network model that includes low- and high-frequency dehazing subnets. Firstly, the hazy image is decomposed using bilateral filters to obtain high- and low-frequency sub-images of the hazy image. Subsequently, the mapping relations between the high- and low-frequency sub-images as well as the high- and low-frequency transmissivity of the hazy image arc learned using the developed network model. The high- and low-frequency transmissivity obtained by model learning is fused to obtain the scene transmissivity of the original hazy image. Finally, the hazy image is restored to the dehazed image based on the atmospheric scattering model, which is trained and tested using the hazy image dataset. The experimental results denote that the proposed method can achieve a good dehazing effect for the synthetic hazy images and real natural hazy images and that it is superior to other contrast algorithms in subjective and objective evaluations.