A Variable Sliding Window Video Super-resolution Method Based On Swin Transformer

With the growing demand for high-definition video in applications such as surveillance, streaming, and virtual reality, video super-resolution (VSR) has become a key technology. Due to the temporal correlation between frames in video, the VSR task needs to focus on how to capture and exploit the inter-frame dependencies in its design in order to solve problems such as occlusion and temporal misalignment, which are important features that distinguish it from single-image super-resolution. In this paper, we propose a variable sliding window VSR method based on Swin Transformer to address temporal misalignment and varying motion patterns in video sequences. By dynamically adjusting attention window sizes based on motion intensity, the model effectively balances local detail enhancement and long-range dependency modeling. The framework includes motion estimation, adaptive patch alignment, and multi-frame self-attention fusion, optimized using the Charbonnier loss. Our method was evaluated on the REDS4 dataset and compared with other baseline VSR approaches using PSNR and SSIM. This adaptive design enhances both reconstruction quality and computational efficiency, offering a robust solution for high-fidelity video restoration.