By Peter W. Hawkes (Ed.)
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Extra resources for Advances in Electronics and Electron Physics, Vol. 87
This view of iterative superresolution methods through a neural network formalism leads to the development of improved algorithms based on a more versatile class of optimization criteria that generalizes distances of the form (Jones and Trutzer, 1989, 1990) where one finds a Q ( x ) that is an estimate of the target or image feature, is consistent with the available data, and minimizes the distance to a prior estimate, P ( x ) , for that target. For given choices o f f ( . ) many distance measures already studied in the literature can be obtained, such as Burg entropy (Burg, 1967) and cross-entropy (Shore and Johnson, 1980).
45), is achieved. As the settling accuracy increases, the neural inverse should, and indeed does, tend toward an unregularized inverse. , 1993; Steriti and Fiddy, 1993). The properties of the inverse are very well conditioned for a wide range of settling accuracies, and the iterative procedure requires no action on the part of the user except for the selection of the stopping point through this parameter. We find that the sets of inverses obtained by S V D for a range of values of the regularization parameter, and by the neural approach for a range of values of the settling accuracy, are quite different in their structure, their distribution of singular values, and their properties when applied to image restoration.
1989). “Computationally feasible high-resolution minimumdistance procedures which extend the maximum-entropy method,” Inverse Problems 5, 749-766. Jones, L. , and Trutzer, V. (1990). “On extending the orthogonality property of minimum norm solutions in Hilbert space to general methods for linear inverse problems,” Inverse problems 6, 379-388. , and Gualtieri, J. A. (1992). “Solving inversion problems with neural networks,” Proc. IJCNN ’92, Baltimore, Vol. 111, p. 955. , New Jersey. , ed. (1991).
Advances in Electronics and Electron Physics, Vol. 87 by Peter W. Hawkes (Ed.)