A dual-stage image deraining network based on rain streak mask suppression and non-local reconstruction collaboration was proposed to address severe rain streak noise interference and insufficient spatial global modeling capability of existing attention mechanisms in single-image deraining networks. In the first stage of the network, a rain streak mask attention mechanism was designed, in which rain streak masks were generated through morphological operations, to enhance the model’s ability to suppress rain streak interference by selectively masking rain-affected regions during feature extraction. In the second stage, a non-local attention mechanism was devised by employing a feature clustering-based non-local similarity measurement method to guide pixel rearrangement, which broke spatial constraints, thereby augmenting the long-range modeling capability of the sliding window attention mechanism and improving the deraining performance. Through progressive optimization based on the dual-stage “rain streak suppression-detail reconstruction” process, high-quality reconstruction of rain-free images was achieved. Experimental results on multiple public datasets demonstrate that the proposed network achieves significant improvements in both PSNR and SSIM metrics compared to other networks, effectively removing rain streaks while better preserving image details and producing high-quality restored results with natural-looking appearance and fine-grained texture representations.