Computer-generated correlated noise images for various statistical distributions

Holly Wenaas; Arthur Robert Weeks; Harley R. Myler

doi:10.1117/12.48398

1 October 1991 Computer-generated correlated noise images for various statistical distributions

Holly Wenaas, Arthur Robert Weeks, Harley R. Myler

Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48398
Event: San Diego, '91, 1991, San Diego, CA, United States

Abstract

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.

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Holly Wenaas, Arthur Robert Weeks, and Harley R. Myler "Computer-generated correlated noise images for various statistical distributions", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48398

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