Noise estimation for computer-generated images using an interval type-2 fuzzy sets filter

Abstract

Global illumination methods based on stochastic techniques provide photo-realistic images. However, they are prone to stochastic noise that can be reduced by increasing the number of paths as proved by Monte Carlo theory. The problem of finding the number of paths that are required in order to ensure that human observers cannot perceive any noise is still open. This paper proposes a new noise estimator, based on interval type-2 fuzzy sets (IT2 FSs) and devoted to computer-generated images. This model can then be used in any progressive stochastic global illumination method in order to estimate the noise level of different parts of any image. A comparative study of this model with a simple test image demonstrates the good consistency between an added noise value and the results from the noise estimator. The proposed noise estimator results have been too compared with full-reference quality measures (or faithfullness measures) like SSIM and gives satisfactory performance.