Abstract: Aiming at the problems of detail loss, color distortion and contrast reduction during image acquisition in foggy environments, we proposed an image dehazing algorithm based on attention feature fusion. Firstly, the algorithm adopted the principle of attention mechanism to design a feature fusion module that  combined channel attention and pixel attention. By using  the characteristics of different channel feature weighting information and uneven distribution of haze in different pixels,  different weights were assigned to the feature map according to the importance of the feature map, which solved the problems of fog residue and color distortion in the traditional  algorithms. Secondly, the enhancement strategy of “strength-operation-subtract” was added to the decoder of the network to solve the problem of image detail loss after dehazing. Finally,  in order to restore the image quality better,   the hybrid loss function was used to  train the network parameters. The experimental results show that the PSNR value of the proposed algorithm is improved by 1.88 dB compared with the comparison algorithm on the public  RESIDE dataset.

Key words: image dehazing, feature fusion, attention mechanism, enhanced model, haze weather