Video Restoration

Video restoration aims to enhance the quality of degraded videos by removing artifacts like noise, blur, and compression artifacts, ultimately recovering clearer, sharper footage. Current research heavily utilizes deep learning, focusing on architectures like transformers and recurrent neural networks to effectively model both spatial and temporal dependencies within video sequences, often incorporating techniques like optical flow estimation and attention mechanisms for improved alignment and feature extraction. These advancements are significant for various applications, including improving the quality of user-generated content, enhancing surveillance footage, and enabling better performance in challenging imaging conditions like atmospheric turbulence or low-light environments. The development of new benchmark datasets and the exploration of zero-shot methods are also driving progress in the field.