On the problem of stable image restoration

Mathematics

Scientific paper

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Functionals, Image Processing, Image Reconstruction, Point Spread Functions, Principal Components Analysis, Statistical Analysis, Correlation, Eigenvalues, Eigenvectors, Matrices (Mathematics), Modulation Transfer Function

Scientific paper

Information about an object that is contained in its blurred image is usually extremely poor, making it difficult to obtain an accurate inverse solution. For this reason only some functionals of the object can be recovered, which give, in general, a more rough description. It is natural to choose, as functionals, the principal components, that is the linear combinations of the parameter estimates which contain the main bulk of information about the object. We show that the principal components can be found by orthogonal transform, based on the eigenvectors of the Fisher informational matrix I. A simple way to compute the needed eigenvalues and eigenvectors of I is considered. The Image Randomness Test is applied to test the feasibility of an object's estimate. As a result, the stable estimate of the object can be constructed by using only the inner resources of the theory, without referring to the Bayes way of consideration. An algorithm of image restoration is described, which takes into account both the photon noise and non-negativity of the object, and is valid for the space-variant Point Spread Function. The same treatment can be applied to other inverse problems.

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