Proceedings PaperMaximum Entropy Restorations Of Ganymede
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Until recently, the maximum entropy (ME) restoring method required too much computer time to be implemented on two-dimensional pictures. Additionally, it was originally derived assuming specifically additive noise in the image, whereas in many cases Poisson noise is a more realistic model. In this paper we show how a ME method may be derived on the basis of maximum likelihood and Poisson statistics for the image data, along with "maximum ignorance" for the object statistics. Interestingly, the new object estimate retains the old form of e to a series, while the noise estimate is now of a multiplicative form, i.e., proportional to the signal image. Also, although the old approach required knowledge of a parameter B, the most negative noise value, the new approach does not require this input. The new ME algorithm was applied to two 23 x 33 data point images of Ganymede. These were acquired by the NASA Pioneer 10 mission. By programming the restoring algorithm with close to maximal efficiency with regard to speed, we were able to bring CDC 6400 computer time down to about 30 sec for each restoration. We also restored the two images linearly, so as to compare performance of the ME algorithm with a standard method. The usual resolution advantages of the ME approach are observed in the outputs.