Showing papers by "Aggelos K. Katsaggelos published in 1988"
TL;DR: A synchronous VLSI implementation of an iterative image restoration algorithm based on a single-step, as well as on a multistep iterative algorithm derived from the single- step regularized iterative restoration algorithm.
13 citations
11 Apr 1988
TL;DR: Based on experiments the authors conclude that the incorporation of the interchannel correlation into the algorithms does not necessarily improve the quality of the restored images over the algorithms that ignore the inter channel correlation, but they result in considerable computation savings.
Abstract: Iterative techniques for restoring colour images are presented. These iterative algorithms have been developed for the restoration of monochromatic images. The colour images are modeled as three spatially related monochromatic images or channels. The interchannel correlation is incorporated into the iterative restoration algorithms in an indirect and in a direct way. Experimental results with simulated and real photographically blurred images are presented. Based on experiments the authors conclude that the incorporation of the interchannel correlation into the algorithms does not necessarily improve the quality of the restored images over the algorithms that ignore the interchannel correlation, but they result in considerable computation savings. >
10 citations
11 Apr 1988
TL;DR: The authors concentrate on regularizing the differentiation operation and incorporating it in an edge detection scheme, and consider both a direct implementation of Miller regularization and one involving projections onto convex sets.
Abstract: It has been observed that the edge detection problem in image processing is ill-posed. The ill-posed aspect of this problem is the differentiation step, which is an explicit operation in many schemes for finding edges in images. Differentiation is very sensitive to noise and its discrete version is an ill-conditioned operation. The authors concentrate on regularizing the differentiation operation and incorporating it in an edge detection scheme. They consider both a direct implementation of Miller regularization and one involving projections onto convex sets. >
7 citations
25 Oct 1988
TL;DR: An adaptive window size method is followed in the implementation of the linear adaptive algorithm, rithm, which improves the restoration results and a method to update the computation of the measure of the bcal activity only in the near edge areas is proposed, thus resulting in great computational savings.
Abstract: In this paper fast adaptive iterative image restoration algorithms are proposed. These algorithms are based on a class of nonadaptive restoration algorithms ihich exhibit a first or higher order of convergence and some of them consist of an on-line and an off-line -computational part. Since only the linear algorithm can take a computationally feasible adaptive formulation, an iterative algorithm which combines the linear adaptive and the higher order nonadaptive algorithms is proposed. An adaptive window size method is followed in the implementation of the linear adaptive algorithm, rithm, which improves the restoration results. A method to update the computation of the measure of the bcal activity only in the near edge areas is also proposed, thus resulting in great computational savings. Finally, experimental results are presented.
4 citations
11 Apr 1988
TL;DR: A class of iterative image restoration algorithms is derived based on a representation theorem for the generalized inverse of a matrix which can be applied to the restoration of signals of any dimensionality.
Abstract: A class of iterative image restoration algorithms is derived. The algorithms are based on a representation theorem for the generalized inverse of a matrix. These algorithms exhibit a first or higher order of convergence and some of them consist of an online and an offline computation part. The conditions for convergence and the range of convergence of these algorithms are derived. An iterative algorithm is also presented which exhibits a higher rate of convergence than the standard quadratic algorithm with no extra computational load. These algorithms can be applied to the restoration of signals of any dimensionality. Iterative restoration algorithms that have appeared in the literature represent special cases of the class of algorithms described. Therefore, the approach presented unifies a large number of iterative restoration algorithms. >
3 citations
01 Jan 1988
TL;DR: In this paper a class of iterative image restoration algorithms is derived based on a representation theorem for the generalized inverse of a matrix that can be applied to the restoration of signals of any dimensionality.
Abstract: In this paper a class of iterative image restoration algorithms is derived based on a representation theorem for the generalized inverse of a matrix. These algorithms exhibit a first or higher order of convergence and some of them consist of an "on-line" and an "off-line" computational part. The conditions for convergence and the rate of convergence of these algorithms are derived. A new iterative algorithm is also presented which exhibits a higher rate of convergence than the standard quadratic algorithm with no extra computational load. These algorithms can be applied to the restoration of signals of any dimensionality. Iterative restoration algorithms that have appeared in the literature represent special cases of the class of algorithms described here. Therefore, the approach presented here unifies a large number of iterative restoration algorithms.