Abstract
The maximum entropy signal-image analysis is summarized with the emphasis of the maximum entropy principle. Shannon-Burg's maximum entropy theory in data processing is a formal extension of Boltzman-Planck's theory. Under a constraint relating the unknown spectrum or image to the data, the minimization of Kullback-Leibler mean information with a uniform prior probability yields a mathematical model of spectrum or image having no physical basis. The statistical techniques of model adaptation lead to practically important advantages. Examples are shown on the spectral measurement and the image reconstruction, which have been successfully applied to the controlled nuclear fusion research.
