To address the issues of high model complexity and poor feature extraction performance in Convolutional neural network-based dehazing algorithms， this paper proposes an end-to-end image dehazing algorithm based on joint mapping of two-branch features. Firstly， the atmospheric scattering model is transformed to separate the mixed-parameter feature and the single-parameter feature model. Then two feature extraction networks， MPFEM and SPFEM are designed according to the two-branch features and the outputs are weighted by two attention mechanisms. Finally， the extracted two-branch features are sent to the restoration module to restore the clear image and perform color-enhancing to obtain the final restored effect. To avoid the loss of texture details caused by using a single loss function in the model training process， multi-scale structure similarity and mean absolute error weighting are used as the loss function. Experimental results show that the proposed algorithm has a simple network structure， obvious dehazing effect， accurate color brightness restoration， and strong edge preservation.