Denoising Process

Denoising, the process of removing unwanted noise from signals or images to reveal underlying patterns, is a fundamental problem across numerous scientific disciplines. Current research focuses on developing advanced denoising techniques using deep learning models, such as U-Nets, diffusion models, and plug-and-play algorithms, often integrating denoising with other tasks like classification or demosaicing for improved efficiency and robustness. These advancements are significantly impacting various fields, from medical imaging (e.g., enhancing OCT scans) and bioacoustics (denoising animal vocalizations) to improving the accuracy and efficiency of machine learning models themselves. The development of novel architectures and algorithms continues to push the boundaries of denoising performance and applicability.