By Peter W. Hawkes (Ed.)
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Additional resources for Advances in Electronics and Electron Physics, Vol. 87
A) Original image. (b) DFT estimate. B. B. Of concern are the quality of the reconstructed image, the ease with which an acceptable matrix inverse is found using this approach (as compared to a more traditional one), and the computational complexity. A solution of the form of Eq. (17) requires the inversion of a matrix composed of elements taken from the Fourier transform of the prior estimate p ( t ) . We consider here the use of only support constraints representing prior knowledge about the true image.
Thesis, University of London. Darling, A. , Hall, T. , and Fiddy, M . A. (1983). “Stable, noniterative, object reconstruction from incomplete data using a priori data,” J . O . S . 73, 1466-1469. Donoho, D. , and Johnstone, I. M. (1989). “Projection-based approximation and a duality with kernel methods,” Annuls of’ Statistics 17(1), 58- 106. , and Malsburg, C. v. , eds. (1987). Neural Computers, NATO AS1 Series F, Vol. 41, Springer Verlag, Berlin. , and Stojancic, M. (1987). “Superresolving signal and image restoration using a linear associative memory,” Appl.
Approximation theoretic derivation of logarithmic entropy principles for inverse problems and unique extension of the maximum entropy method to incorporate prior knowledge,” S l A M J . Appl. Math. 49, 650-661. Jones, L. , and Byrne, C. L. (1990). “General entropy criteria for inverse problems, with applications to data compression, pattern classification and cluster analysis,” IEEE Trans. IT^, 23-30, Jones, L. , and Trutzer, V. (1989). “Computationally feasible high-resolution minimumdistance procedures which extend the maximum-entropy method,” Inverse Problems 5, 749-766.