  This paper aims to explore the evolution of image denoising in a
pedagological way. We briefly review classical methods such as Fourier analysis
and wavelet bases, highlighting the challenges they faced until the emergence
of neural networks, notably the U-Net, in the 2010s. The remarkable performance
of these networks has been demonstrated in studies such as Kadkhodaie et al.
(2024). They exhibit adaptability to various image types, including those with
fixed regularity, facial images, and bedroom scenes, achieving optimal results
and biased towards geometry-adaptive harmonic basis. The introduction of score
diffusion has played a crucial role in image generation. In this context,
denoising becomes essential as it facilitates the estimation of probability
density scores. We discuss the prerequisites for genuine learning of
probability densities, offering insights that extend from mathematical research
to the implications of universal structures.
