To explore the dehazing priors and constraints among the physical parameters during imaging under haze conditions and improve dehazing accuracy， we propose a decomposition–composition and recurrent refinement network based on the physical imaging model for image dehazing. Unlike existing dehazing methods， it contains a transmission prediction branch and a clear image prediction branch. Both branches are built based on the multi-scale pyramid encoder–decoder network with a recurrent unit that can utilize multiscale contextual features and has more complete information exchange. Considering the transmission map is related to the scene depth and haze concentration， the transmission map can be regarded as a haze concentration prior and guide the clear image prediction branch to estimate and refine the dehazing result recurrently. Similarly， the clear image that contains the scene depth information is regarded as a depth prior and guides the transmission map prediction branch to predict and refine the transmission map. Then， the predicted transmission map and clear image are further synthesized as the haze image that serves as the input of the network in each recurrent step， enabling the predicted transmission map and clear image to meet the constraints of the physical imaging model. The experimental results demonstrate that our method not only achieves a good dehazing effect on both synthetic and real images， but also outperforms existing methods in terms of quality and quantity. The average processing time for a single hazy image is 0.037 s， indicating that it has potential application value in the engineering practice of image dehazing.