Joint transmission map estimation and image dehazing using dual vision attention network

To fully exploit the dependency of information captured from transmission map estimation and image dehazing, this paper proposes a dual vision attention network for jointly estimating transmission map and clear image from hazy image. The network consists of an image dehazing layer and a transmission estimation layer, with each layer containing a recurrent attentive network and an encoder-decoder network. The haze attention map is generated in the image dehazing layer under the supervision of the transmission map in the recurrent attentive network and guides the subsequent encoder-decoder network to estimate the dehazing result. Similarly, with clear image supervision, the recurrent attentive network in the transmission estimation layer generates the scene attention map and guides the subsequent encoder-decoder network to predict the transmission map. Based on this architecture, we adopted the recurrent units in the two recurrent attentive networks to exchange hidden information. The haze concentration information can then be used to estimate the scene attention map, and the scene information can be used to predict the transmission map. The experimental results demonstrate that our method not only achieves a good dehazing effect on both synthetic and real images, but also outperforms the existing methods in terms of quality and quantity. The average processing time for a single hazy image is 0.043 s. Therefore, our method can be used in the engineering practice of image dehazing. © 2021, Science Press. All right reserved.