SSVD: Efficient Video Deinterlacing With Spatiotemporal Synchronization and Refinement

Video deinterlacing remains a significant challenge due to structural artifacts and information loss. When displayed on modern digital devices, early interlaced videos often suffer from complex interlacing and compression artifacts, which severely degrade visual quality. Existing deinterlacing methods typically struggle to handle such diverse artifacts while preserving fine-grained details. To address these issues, we propose a novel Spatiotemporal Synchronization for Video Deinterlacing (SSVD). First, we design a Multi-Directional Shuffling Module (MDSM) to enhance the model's ability to capture spatial dependencies, thereby guiding the prediction of missing fields. Second, a Dynamic Cross-frame Interaction Module (DCIM) is incorporated to implicitly model inter-frame correspondences, effectively leveraging cross-frame information to alleviate the blurring and artifacts. Third, we develop a Gated Refinement Module (GRM) to achieve fine-grained reconstruction. Experimental results demonstrate SSVD is superior to the state-of-the-art algorithms in video deinterlacing tasks, achieving superior visual quality and detail reconstruction. Source code will be made public after the review is completed.