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BasicSR is a deep learning framework designed for advanced video restoration tasks such as video super-resolution, deblurring, and denoising. Unlike single-image restoration models, EDVR addresses the temporal dimension by aligning multiple video frames using deformable convolutional layers in a coarse-to-fine manner, allowing it to effectively handle large motion and complex scene dynamics. The architecture includes bespoke modules (e.g., Pyramid, Cascading and Deformable alignment and Temporal Spatial Attention fusion) that align information across frames and emphasize important features for restoration, enabling state-of-the-art performance on benchmarks such as the REDS challenge. By fusing spatial and temporal context, EDVR produces significantly improved visual quality in restored videos compared with approaches that treat each frame independently.
Features
- Video restoration with super-resolution, deblurring, and denoising capabilities
- Enhanced deformable convolution modules for robust frame alignment
- Temporal and spatial attention fusion for high-quality reconstruction
- Handles large motion and complex scene dynamics across frames
- Suitable for research and development of advanced video enhancement systems
- Strong performance on benchmark datasets and competitions
Project Samples
