Abstract:  Aiming at the problems of color distortion and detail blur in current defogging methods， a multi-scale feature image defogging algorithm based on content-guided attention fusion is proposed with encoder-decoder network architecture. Firstly， multi-scale feature extraction module is used to encode， and three parallel expanded convolutions with different scales and squeeze and excitation attention are designed to enlarge the receptor field， extract features of different scales， and improve feature utilization. Secondly， in the decoder， the content-guided attention fusion module is designed to dynamically improve different weights for the deep and the shallow features to retain more effective feature information. Finally， pyramid scene parsing network is introduced to improve the ability of global information acquisition. The experimental results show that compared with other algorithms， the proposed algorithm improves 26.13% and 6.39% on the peak signal-to-noise ratio and structural similarity of SOTS datasets， respectively. The entropy and average gradient of the real fog datasets are increased by 3.27% and 21.09% respectively. The proposed algorithm improves the problem of defog incompleteness and detail blur.

Key words:  , image defogging, feature fusion, attention mechanism, multi-scale features, deep learning