The current defogging algorithm cannot solve the problem of uneven haze image defogging， so this paper proposes a wide self-attention fusion conditional generation against network image defogging algorithm. The wide self-attention mechanism is added to the algorithm， so that the algorithm can automatically assign different weights to the features of different haze regions. The feature extraction part of the algorithm adopts the DenseNet fusion self-attention network architecture. Under the premise of ensuring the maximum information transmission between the middle layers of the network， the DenseNet network directly connects all layers to obtain more context information and make more effective use of the extracted features. Fusion of self-attention can learn complex nonlinearity from the features extracted from the encoder part， and improve the ability of the network to accurately estimate different haze. The algorithm uses Patch discriminator to enhance local and global consistency of defogging images. The experimental results show that the qualitative comparison of the algorithm network on NTIRE 2020， NTIRE 2021 and O-Haze datasets has better visual effects than other advanced algorithms. In the quantitative comparison， compared with the best performance of the selected advanced algorithms， the peak signal-to-noise ratio and the structural similarity index increases by 0.4 and 0.02， respectively.