Optimization by Vector Space Methods
Optimization by Vector Space Methods
A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Images
IEEE Transactions on Computers
An Iterative-Improvement Approach to the Numerical Solution of Vector Toeplitz Systems
IEEE Transactions on Computers
Least Squares Image Restoration Using Spline Basis Functions
IEEE Transactions on Computers
Outer Product Expansions and Their Uses in Digital Image Processing
IEEE Transactions on Computers
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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.