Characterization of a maximum entropy image reconstruction algorithm

Details Characterization of a maximum entropy image reconstruction algorithm Characterization of a maximum entropy image reconstruction algorithm

Aug 1995

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995spie.2564..126l&link_type=abstract

Proc. SPIE Vol. 2564, p. 126-135, Applications of Digital Image Processing XVIII, Andrew G. Tescher; Ed.

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Maximum entropy algorithms (MEM) can be used to restore imagery within a region of corrupted data. The necessary condition being that the point spread function (PSF) must be sufficiently large with respect to the region of corrupted data. In most cases MEM will give a result which may not be the result desired. In general the error assessment is qualitative and the restored image appears cosmetically more pleasing to the yee. This paper presents a characterization of one MEM algorithm which estimates an object consistent with Boltzmann statistics within a corrupted region of the detector array. Chosen as an example will be two prefix Hubble Space Telescope (HST) Faint Object Camera (FOC) images. The characterization consists of an assessment of photometric accuracy, precision, and resolution. The results presented here will be parameterized in terms of signal to noise ratio and size of corrupted data. This study is conducted with a set of simulated data which closely match that of the HST FOC. As a demonstration, we apply these techniques to an actual data set obtained from the HST FOC. These FOC data are corrupted in a region which we restore using a synthetic PSF.

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