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Maximum entropy methods in dark field electron micrographs and elemental maps
1Ontario Cancer Institute, University of Toronto, Ontario, Canada.
Abstract:
A maximum entropy algorithm is described which not only fits a model to the data consistent with the size of the noise and the maximum entropy principle but also distributes the residuals between the data and the model in a way consistent with the noise in the data having been generated by a random gaussian process. The results of applying the algorithm to profiles of electron micrographs, electron micrographs and model data is presented. The algorithm is found to achieve various degrees of signal to noise ratio enhancement. Preliminary results show that the spatial resolution is not suppressed. A biassing artifact is described.