Bayesian filtering of Poisson noise using local statistics

Images recorded at low-light levels inherently suffer from Poisson noise. A filter based on the maximum a posteriori probability (MAP) criterion is developed to remove this noise. The filter is adaptive; it responds to local changes in image statistics and, thus, removes the noise along the edges without significantly affecting the edge sharpness. It does not require any a priori information about the original image because all the parameters needed for the filter are estimated from the noisy image by assuming local stationarity. Additionally, the simple structure of the filter can be easily implemented in hardware