Denoising Model

Denoising models aim to remove unwanted noise from various data types, including images, audio, and even biological signals, improving data quality and enabling more accurate analysis. Current research focuses on developing sophisticated architectures like convolutional neural networks (CNNs), transformers, and diffusion models, often incorporating techniques such as state-space modeling and hybrid regularization to enhance performance and efficiency. These advancements are crucial for improving the accuracy of downstream tasks in diverse fields, from medical imaging diagnosis and autonomous driving to bioacoustic analysis and speech enhancement. The development of self-supervised and even zero-shot methods is also a significant trend, reducing reliance on large, clean datasets.