Abstract
Weighted least-squares algorithms are proposed for reconstructing images from photon-limited data captured with CCD cameras. These cameras introduce blur and signal-independent noise, while forces of nature give rise to so-called background noise. The errors in the data are addressed by using a weighted least-squares formulation that emphasizes more reliable data values. The performance of the proposed algorithms is investigated by using synthetic and real data obtained from the Space Telescope Science Institute. The real data were captured with the wide-field/planetary camera aboard the original Hubble Space Telescope. Simulation studies with the synthetic data indicate that the proposed algorithms converge faster and have fewer “false stars” than a modified Richardson–Lucy algorithm.
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