Optimal Denoising

Optimal denoising aims to recover clean signals from noisy observations, a crucial task across diverse scientific fields and applications. Current research focuses on developing and analyzing denoising algorithms, including those based on diffusion models, approximate message passing, and deep neural networks like U-nets, with a strong emphasis on achieving theoretically optimal performance, often measured by mean squared error. This pursuit is driven by the need for robust and efficient denoising in various contexts, such as image processing, medical imaging, and sensor data analysis, leading to improvements in data quality and downstream analytical capabilities.