Denoising Task

Denoising, the process of removing unwanted noise from data, is a crucial task across numerous fields, aiming to recover underlying signals or structures. Current research focuses on improving denoising performance in various domains, including images, text, and recommendation systems, employing diverse approaches such as diffusion models, contrastive learning, and specialized loss functions tailored to specific data characteristics (e.g., frequency-domain information for images). These advancements leverage architectures like transformers and UNets, often incorporating attention mechanisms to enhance efficiency and accuracy. The impact of improved denoising techniques extends to applications ranging from enhanced image and video quality to more robust machine learning models and improved recommender systems.