Denoising Algorithm

Denoising algorithms aim to remove noise from various data types, primarily images and signals, to improve clarity and accuracy. Current research emphasizes the development of sophisticated models, including deep neural networks (like transformers and convolutional neural networks), state-space models, and implicit neural representations, often incorporating techniques like self-similarity and unbiased risk estimation to enhance performance and adaptability across diverse noise types and data modalities. These advancements are crucial for improving the quality of medical imaging, underwater acoustic communication, and computer vision applications, as well as optimizing resource-constrained systems like UAVs. The field is also exploring joint image and noise modeling to achieve more robust and generalizable denoising.