Abstract: Single-image rain removal is a crucial task in computer vision, aiming to eliminate rain streaks from rainy images and generate high-quality rain-free images. Current deep learning-based multi-scale rain removal algorithms face challenges in capturing details at different scales and neglecting information complementarity among scales, which can lead to image distortion and incomplete rain streak removal. To address these issues, this paper proposes an image rain removal network based on cross-scale attention fusion, aiming to remove dense rain streaks while preserving original image details to improve the visual quality of the rain removal image. The rain removal network consists of three sub-networks, each dedicated to obtaining rain pattern information at different scales. Each sub-network is composed of densely connected cross-scale feature fusion modules. The designed module takes the cross-scale attention fusion as the core, which establishes inter-scale relationships to achieve information complementarity and enables the network to consider both details and global information. Experimental results demonstrate the effectiveness of the proposed model on synthetic datasets Rain200H and Rain200L. The peak signal-to-noise ratio (PSNR) of the derained images reaches 29.91/39.23 dB, and the structural similarity index (SSIM) is 0.92/0.99, outperforming general mainstream methods and achieving favorable visual effects while preserving image details and ensuring natural rain removal.