The evaluation of image processing algorithms generally assumes images that are degraded by known statistical noise. The type of noise distributions that are needed depend on the nature of the application. The noise distributions that are commonly used are the Gaussian, negative exponential, and uniform distributions. Typically, these computer-generated noise images are spatially uncorrelated. It is the purpose of this paper to present computer-generated two- dimensional correlated and uncorrelated noise images that can be readily used in the evaluation of various image processing algorithms. Several statistical distributions including the negative exponential, the Rayleigh, and the K-distribution are generated from Gaussian statistical noise and are presented. For the generation of correlated noise images, the correlation function is defined by either describing the correlation function directly or by specifying the power spectral density function (PSD) using the Weiner-Kinchine theorem. These computer synthesized images are then compared against the expected theoretical results. Additionally, the autocorrelation function for the computer-generated noise images are computed and compared against the specified autocorrelation function. Also, included in the theoretical analysis is the effect of quantization, and finite pixel intensity, i.e., 0 - 255. Finally, several uncorrelated and correlated noise images are presented.

Date Published: 1 October 1991
PDF: 12 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48398

Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)