SAT: shift alignment transformer for video denoising without flow estimation

Video denoising aims to recover clean video sequences with rich structural details from temporally correlated noisy frames, where the core challenge lies in effectively aggregating long-range temporal information under complex motion conditions. Existing sliding-window and recurrent-based approaches suffer from limited receptive fields or restricted parallelism, while recent Transformer-based methods, although capable of modeling long-range dependencies, are constrained by fixed-window designs that hinder large-scale motion handling. In this paper, we propose a novel and efficient Shift Alignment Transformer (SAT) for video denoising, which enables implicit feature alignment and aggregation over extended spatiotemporal regions without relying on optical flow estimation. The proposed framework introduces two complementary mechanisms: Time Segment Shift, which establishes inter-frame correlations by dynamically shifting temporal windows, and Local Window Shift, which enhances intra-frame contextual modeling through spatial window displacement. Together, these designs enable flexible receptive field expansion while maintaining computational efficiency. Extensive experiments conducted on both synthetic Gaussian noise removal in the RGB domain and real-world video denoising in the RAW domain demonstrate that SAT consistently outperforms or matches state-of-the-art methods in terms of denoising accuracy, while achieving a favorable balance between performance and computational cost. These results indicate that the proposed SAT provides an effective and practical solution for long-range spatiotemporal modeling in video denoising, offering a robust alternative to explicit motion estimation and fixed-window Transformer architectures.