Unsupervised Denoising

Unsupervised denoising aims to remove noise from images or other data without relying on paired clean/noisy examples, a significant challenge in many scientific fields. Current research focuses on developing robust unsupervised methods using architectures like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and implicit neural representations, often incorporating techniques like temporal filtering or frequency domain analysis to improve performance. These advancements are crucial for analyzing data from various modalities (e.g., microscopy, medical imaging) where obtaining clean ground truth data is difficult or impossible, enabling more accurate and reliable scientific analysis and clinical applications.