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M.P. Ekstrom, Lawrence Livermore Laboratory, University of California
Numerical optimization techniques are applied to the identification of linear, shift-invariant imaging systems in the presence of noise. The approach used is to model the available or measured image of a real known object as the planar convolution of object and system-spread function and additive noise. The spread function is derived by minimization of a spatial error criterion (least squares) and characterized using a matric formalism. The numerical realization of the algorithm is discussed in detail; the most substantial problem encountered being the calculation of a vector-generalized inverse. This problem is avoided in the special case where the object scene is taken to be decomposable.
Index Terms:
Image restoration, numerical deconvolution, spread-response function, system identification, Toeplitz matrices, vector-generalized inverse.
M.P. Ekstrom, "A Numerical Algorithm for Identifying Spread Functions of Shift-Invariant Imaging Systems," IEEE Transactions on Computers, vol. 22, no. 4, pp. 322-328, April 1973, doi:10.1109/T-C.1973.223718
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