Estimation of Image Signals with Poisson Noise
UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES IMAGE PROCESSING INST
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An optimal filter in the sense of maximum a posteriori probability MAP is derived for image signals detected at low light levels. These signals suffer from Poisson noise and blurring degradations. The low level photon resolved image signal is modeled as an inhomogeneous Poisson point process. The photon noise is inherent in any detected image, and is particularly serious at low light levels. At these low light levels, the emission of photons is described by a Poisson point process, with the average rate of emission proportional to the integrated intensity. The blurring degradation model in the system includes space-variant and space-invariant effects uch as atmospheric turbulence, linear motion, diffraction, and aberrations. The estimation is performed assuming that the photon events counted in each detector are independent, Poisson distributed random processes for the large time-bandwidth product case.
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