Image Denoising with Stochastic Differential Equations
||Image Denoising with Stochastic Differential Equations|
||Ziwei Luo <firstname.lastname@example.org>|
||2022-11-21 – 2023-06-01|
The Wiener process is to add continuous Gaussian noises to a clean signal, which constructs the dispersion part of a Stochastic Differential Equation (SDE). In this project, we model the image noise-adding process as a conditional Wiener process where the noisy image is an intermediate state and the transition follows a simple SDE. Naturally, we can reverse the SDE to get the initial state image. Its drift function of the reverse-time SDE can be derived from the original forward SDE with an additional score function which can be approximated with a deep convolutional neural network (CNN). This project aims to breakthrough the current frameworks of image denoising, leading to a better result in realistic image/signal restoration.