Data acquired with a CCD camera are modeled as an additive Poisson–Gaussian mixture, with the Poisson component representing cumulative counts of object-dependent photoelectrons, object-independent photoelectrons, bias electrons, and thermoelectrons and the Gaussian component representing readout noise. Two methods are examined for compensating for readout noise. One method relies on approximating the Gaussian readout noise by a Poisson noise and then using a modified Richardson–Lucy algorithm to effect the compensation. This method has been used for restoring images acquired with CCD’s in the original Wide-Field/Planetary Camera aboard the Hubble Space Telescope. The second method directly uses the expectation-maximization algorithm derived for the Poisson–Gaussian mixture data. This requires the determination of the conditional-mean estimate of the Poisson component of the mixture, which is accomplished by the evaluation of a nonlinear function of the data. The second method requires more computation than the first but is more accurate mathematically and yields modest improvements in the quality of the restorations, particularly for fainter objects. As a specific example, we compare the two methods in restorations of images representative of those acquired with that camera; they contain excess blurring that is due to spherical aberration and a rms readout noise level of 13 electrons.
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