Aiming at the problem that a single intensity image lacks polarization information and cannot provide sufficient scene information under bad weather conditions, this paper proposes a dual-attention mechanism to generate an adversarial network for fusion of intensity and polarization images. The algorithmic network consists of a generator containing an encoder, a fusion module and a decoder and a discriminator. First, the source image is fed into the encoder of the generator, after a convolutional layer and dense block for feature extraction, then feature fusion is performed in the texture enhancement fusion module containing the attention mechanism and finally the fused image is obtained by the decoder. The discriminator is mainly composed of two convolutional modules and two attention modules, and the generator network parameters are iteratively optimised by constant gaming during the network training process, so that the generator outputs a high-quality fused image that retains the sparse features of the polarimetric image without losing the intensity image information. Experiments show that the fused images obtained by this method are subjectively richer in texture information and more in line with the visual perception of the human eye, and that the SD is improved by about 18.5% and the VIF by about 22.4% in the objective evaluation index.