Issue No. 04 - April (1973 vol. 22)
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.
Image restoration, numerical deconvolution, spread-response function, system identification, Toeplitz matrices, vector-generalized inverse.
M. Ekstrom, "A Numerical Algorithm for Identifying Spread Functions of Shift-Invariant Imaging Systems," in IEEE Transactions on Computers, vol. 22, no. , pp. 322-328, 1973.