Title :
A one-parametric reduced filter for image restoration
Author :
Selkäinaho, Kalevi
Author_Institution :
Dept. of Comput. Sci. & Appl. Math., Kuopio Univ., Finland
Abstract :
The linear algebraic restoration filters for discrete linear degradation models with additive noise are n2×n2 matrices for n×n images in general. In this paper, a new reduced filter is derived which is realized by products of n×n matrices and for which the regularization parameter is easy to obtain. Although being computationally more economic, its restoring power has proved to be somewhat better than that of the two filters, with different dimensions, which are compared here. Each of the filters are regularizations of the circuit of the Gauss-Markov theorem, for example in the case of circulant matrices or white noise
Keywords :
image restoration; Gauss-Markov theorem; additive noise; circulant matrices; discrete linear degradation models; image restoration; one-parametric reduced filter; optimal regularization parameter; white noise; Degradation; Eigenvalues and eigenfunctions; Erbium; Filters; Gaussian noise; Image restoration; Least squares approximation; Least squares methods; Nearest neighbor searches; Pixel;
Conference_Titel :
Pattern Recognition, 1994. Vol. 3 - Conference C: Signal Processing, Proceedings of the 12th IAPR International Conference on
Conference_Location :
Jerusalem
Print_ISBN :
0-8186-6275-1
DOI :
10.1109/ICPR.1994.577124
