Video Deblurring

Video deblurring aims to recover sharp video frames from blurry input, a crucial task with applications in various fields. Current research focuses on developing efficient and robust deep learning models, employing architectures like transformers, generative adversarial networks, and diffusion models, often incorporating additional information such as depth maps or gyroscope data to improve accuracy. These advancements leverage techniques like multi-scale processing, alternating optimization, and pseudo-inverse modeling to enhance performance and reduce computational cost. The resulting improvements in video quality have significant implications for applications ranging from autonomous driving to medical imaging.