Abstract :
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.
Keywords :
Image restoration, numerical deconvolution, spread-response function, system identification, Toeplitz matrices, vector-generalized inverse.; Additive noise; Convolution; Image restoration; Layout; Least squares methods; Matrix decomposition; Noise measurement; Optical imaging; System identification; Transmission line matrix methods; Image restoration, numerical deconvolution, spread-response function, system identification, Toeplitz matrices, vector-generalized inverse.;
