Abstract: Abstract： In hazy weather， visible light scattering and absorption occur when passing through the atmosphere， resulting in poor image quality， information blocking or loss. Based on this， we propose an improved generative adversarial network （GAN） image dehazing algorithm， which learns to generate dehazed images in the generator and discriminator adversarial. In the generator， a three-row multi-column multi-scale fused attention network （Grid-G） is proposed to introduce channel attention and pixel attention to process the thick haze region and high frequency region of the image from different angles， respectively. In the discriminator， the high and low frequency information in the image is introduced to construct the fused discriminator （FD-F）， which is used as a source of additional a priori discriminative images. Experiments on synthetic and real data in the RESIDE dataset show that the algorithm outperforms the rest of the comparison algorithms in terms of peak signal-to-noise ratio and structural similarity， achieves better dehazing effects， and effectively improves problems such as color distortion.

Key words: Key words： image dehazing, generative adversarial network, fusion discriminator