The relative importance of being Gaussian

TL;DR

This paper investigates the robustness of diffusion-based denoising algorithms when the noise distribution deviates from Gaussian, highlighting the importance of Gaussian properties for their effectiveness.

Contribution

It provides an analysis of how diffusion algorithms perform with non-Gaussian noise, emphasizing the critical role of Gaussian assumptions in their success.

Findings

Algorithms perform poorly with non-Gaussian noise.

Gaussian properties are crucial for the theoretical justification of these algorithms.

Experiments on small-scale setups confirm the importance of Gaussian assumptions.

Abstract

The remarkable results for denoising in computer vision using diffusion models given in \cite{SDWMG,HJA,HHG} yield a robust mathematical justification for algorithms based on crucial properties of a sequence of Gaussian independent $N(0,1)$ random variables. In particular the derivations use the fact that a Gaussian distribution is determined by its mean and variance and that the sum of two Gaussians is another Gaussian. \bigskip The issue raised in this short note is the following: suppose we use the algorithm without any changes but replace the nature of the noise and use, for instance, uniformly distributed noise or noise with a Beta distribution, or noise which is a random superposition of two Gaussians with very different variances. One could, of course, try to modify the algorithm keeping in mind the nature of the noise, but this is not what we do. Instead we study the performance of the algorithm when used with noise that is very far in nature from the Gaussian case, where it is designed to work well. Usually these algorithms are implemented on very powerful computers. Our experiments are all carried out on a small laptop and for the smallest possible image size. Exploring how our observations are confirmed or changed when dealing in different situations remains an interesting challenge.