Raindrop image enhancement methods based on deep learning generally have some problems, such as highly dependent on aligned sample datasets, and blurred background detail after removing raindrops. In this regard, this paper proposes a weakly supervised raindrop image enhancement method guided by dual attention. This method designs and constructs a weakly supervised raindrop image enhancement network. It only needs images from the raindrop image domain and the clean image domain for training, which can effectively reduce the dependence on the aligned sample datasets. At the same time, dual attention is introduced into the generation network to guide the feature extraction and multi-branch masks generation. After the masks are fused with the input raindrop image, a clean image with a clear background is obtained, and the input raindrop image is enhanced. The experimental results show that the PSNR is 27.0711 dB and the SSIM is 0.8996 of the proposed method respectively on the Raindrop. The background details and color information of the image are better preserved than the previous methods, which prove the feasibility and effectiveness of the proposed method.