Singular Value Decomposition And Digital Image Processing
Methodi Kovatchev, Eugene Mitev, Rumiana Nedkova · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
Two methods for decreasing variation due to additive noise into an image are discussed. Both methods are based on Singular Values Decomposition (SVD) of given Image matrix: • The singular values take the meaning of the dispersion coefficients, and the Image reconstruction by part of the basis functions leads to entropy minimization of the image, guaranteeing minimization of the least-squares error. The sharing criterion is used by the first method to extract the most significant coefficients. • Another discussed method Is a filter fitting to the singular value spectrum of a noisy matrix. In the case of known noise distribution the filter is noise matched.