Video Denoising

Video denoising aims to remove noise from video sequences, improving visual quality and enabling clearer analysis. Current research focuses on developing efficient and effective deep learning models, including recurrent neural networks (RNNs), transformers, and multiple-input-multiple-output (MIMO) architectures, often incorporating techniques like optical flow estimation and attention mechanisms to leverage temporal information. These advancements are driven by the need for real-time processing and improved performance on diverse noise types, impacting applications such as video conferencing, medical imaging, and surveillance. Furthermore, research explores both supervised and unsupervised learning approaches, addressing the challenge of obtaining large, paired noisy-clean datasets.