Image recognition in the presence of non-Gaussian noise with unknown statistics

We design receivers to detect a known pattern or a reference signal in the presence of very general and non-Gaussian types of noise. Three sources of input-noise degradation are considered: additive, multiplicative, and disjo int background. The detection process involves two steps: (1) estimation of the relevant noise parameters within the framework of hypothesis testing a nd (2) maximizing a certain metric that measures the likelihood of the targ et being at a given location. The parameter estimation portion is carried o ut by moment-matching techniques. Because of the number of unknown paramete rs and the fact that various types of input-noise processes are non-Gaussia n, the methods that are used to estimate these parameters differ from the s tandard methods of maximizing the likelihood function. To verify the existe nce of the target at a certain location, we use l(p)-norm metric for p grea ter than or equal to 0 to measure the likelihood of the target being presen t at the location of interest. Computer simulations are used to show that f or the images tested here, the receivers designed herein perform better tha n some existing receivers. (C) 2001 Optical Society of America.