Analysis and comparison of Gaussian noise denoising algorithms

Abstract

Collecting and processing various images has become an irreversible trend. It is meaningful to conduct more in-depth research on image denoising algorithms. We proposed mean filtering, median filtering, wiener filtering and wavelet filtering to denoise the image with Gaussian noise separately. And the objective image quality assessments are used to evaluate the quality of the images after denoising. Among them, wavelet filtering and Wiener filtering have a better effect on weaken Gaussian noise. Mean filtering and median filtering can also weaken Gaussian noise to some extent but the effect is limited. At the same time, it is equally important to select the appropriate denoising block for the diverse mean and variance of Gaussian noise. In wavelet filtering, the number of layers to be decomposed and the choice of threshold will also affect the effect of image denoising.