Abstract

The singular-value-decomposition (SVD) method has been utilized in an algebraic image-reconstruction problem. The effect of noise on a reconstructed image can be understood from the expansion coefficients of an object in singular vector space calculated by the SVD algorithm. We propose a method in which the coefficients corresponding to small singular values are estimated by using a priori information about the object. Results of computer simulations for iterative restoration of an image subjected to low-pass frequency filtering show the utility of the method.

© 1985 Optical Society of America

Image restoration in singular vector space by the method of convex projections

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