Variational Denoising

Variational Denoising is a computational technique used to remove noise from data, such as images or signals, while preserving essential features. It employs mathematical models to estimate what the clean data should look like, assuming a certain distribution of the noise. By evaluating various possibilities, it finds the most likely clean version of the original data. This method is particularly effective because it balances the removal of noise with the retention of important details, leading to clearer and more accurate results in fields like image processing and data analysis.