Showing papers by "Aggelos K. Katsaggelos published in 1989"
TL;DR: This tutorial paper discusses the use of successive-approximation-based iterative restoration algorithms for the removal of linear blurs and noise from images and regularization is introduced as a means for preventing the excessive noise magnification that is typically associated with ill-posed inverse problems such as the deblurring problem.
Abstract: This tutorial paper discusses the use of successive-approximation-based iterative restoration algorithms for the removal of linear blurs and noise from images. Iterative algorithms are particularly attractive for this application because they allow for the incorporation of prior knowledge about the class of feasible solutions, because they can be used to remove nonstationary blurs, and because they are fairly robust with respect to errors in the approximation of the blurring operator. Regularization is introduced as a means for preventing the excessive noise magnification that is typically associated with ill-posed inverse problems such as the deblurring problem. Iterative algorithms with higher convergence rates and a multistep iterative algorithm are also discussed. A number of examples are presented.
298 citations
01 Nov 1989
TL;DR: In this article, the filtering of noise in image sequences using spatio-temporal motion compensated techniques is considered, and a number of filtering techniques are proposed and compared in this work.
Abstract: In this paper the filtering of noise in image sequences using spatio-temporal motion compensated techniques is considered. Noise in video signals degrades both the image quality and the performance of subsequent image processing algorithms. Although the filtering of noise in single images has been studied extensively, there have been few results in the literature on the filtering of noise in image sequences. A number of filtering techniques are proposed and compared in this work. They are grouped into recursive spatio-temporal and motion compensated filtering techniques. A 3-D point estimator which is an extension of a 2-D estimator due to Kak [5] belongs in the first group, while a motion compensated recursive 3-D estimator and 2-D estimators followed by motion compensated temporal filters belong in the second group. The motion in the sequences is estimated using the pel-recursive Wiener-based algorithm [8] and the block-matching algorithm. The methods proposed are compared experimentally on the basis of the signal-to-noise ratio improvement and the visual quality of the restored image sequences.
43 citations
01 Nov 1989
TL;DR: Algorithms for the simultaneous identification of the blur and the restoration of a noisy blurred image are presented and experiments with simulated and photographically blurred images are shown.
Abstract: Algorithms for the simultaneous identification of the blur and the restoration of a noisy blurred image are presented in this paper. The original image and the additive noise are modeled as zero-mean Gaussian random processes, which are characterized by their covariance matrices. The covariance matrices are unknown parameters. The blurring process is specified by its point spread function, which is also unknown. Maximum likelihood estimation is used to find these unknown parameters. In turn, the EM algorithm is exploited to find the maximum likelihood estimates. In applying the EM algorithm, the original image is chosen to be part of the complete data; its estimate, which represents the restored image, is computed in the E-step of the EM iterations. Explicit iterative expressions are derived for the estimation of relevant parameters. Experiments with simulated and photographically blurred images are shown.
11 citations
03 Apr 1989
TL;DR: In this article, the original image and the additive noise are assumed to be zeroniean Gaussian random processes and maximum likelihood estimation is used to find those unknown parameters.
Abstract: 'I'his paper deals with simultaneous identification and restoration of images. By idqitification, we mean the estimation of the parameters characterizing the degradation mechanisms. The original image and the additive noise are assumed to be zeroniean Gaussian random processes. Their autocovariance ma trices arc unknown parameters. Blurring is part of the degradation. It is specified by its point spread function, which is also an unknown parameter to be estimated. Maximum likelihood estimation is used to find those unknown parameters. In turn, the EM algorithm is used to find the maximum likelihood estimates. In applying the EM algorithm, the observed image is treated as the incomplete data, which turns out to be a linear transformation of the complete data. Diffefent choices of complete data are investigated. Under the assumptioil that the image covariance and distortion matrices are circulant, the estimation of the unknown parameters becomes feasible. Explicit iterative expressions are derived for the estimation. The restored image is computed in the E-step of the EM algorithm.
7 citations
14 Aug 1989
TL;DR: Three parallel iterative image restoration algorithms with and without preconditioning are proposed and analyzed and it is shown that the second and third algorithms can give a speed-up proportional to the number of processors when proper assumptions are satisfied.
Abstract: Three parallel iterative image restoration algorithms with and without preconditioning are proposed and analyzed. The first algorithm corresponds to a coarse-grained general-purpose multiprocessor computer, the second to a massively parallel computer with synchronization, and the third to a massively parallel computer without synchronization. It is shown that the second and third algorithms can give a speed-up proportional to the number of processors when proper assumptions are satisfied, while the first one performs the best when simulated by a uniprocessor computer. >
6 citations
01 Nov 1989
TL;DR: It is concluded that the multiple frame Wiener-based algorithm performs better than the two-frame Wieners-based pel-recursive algorithm with respect to robustness, stability, and smoothness of the velocity field.
Abstract: In this paper, a multiple frame formulation of the pel-recursive Wiener-based displacement estimation algorithm [1] is presented The derivation of the algorithm is based on the assumption that both the so-called update of the initial estimate of the displacement vector and the linearization error are samples of stochastic processes A linear least-squares estimate of the update of the initial estimate of the displacement vector from the previous frame to the current is provided, based on w observations in a causal window W of each of the v previous frames The sensitivity of the pel-recursive algorithms in the areas where occlusion occurs is studied and their performance is improved with adaptive regularization of the inverse problem that is involved Based on our experiments with typical video-conferencing scenes, we concluded that the multiple frame Wiener-based algorithm performs better than the two-frame Wiener-based pel-recursive algorithm with respect to robustness, stability, and smoothness of the velocity field
6 citations
06 Sep 1989
TL;DR: In this article, the image and the noise have been modeled as multivariate Gaussian processes and maximum likelihood estimation has been used to estimate the parameters that characterize the Gaussian process, where the estimation of the conditional mean of the image represents the restored image.
Abstract: Summary form only given. Simultaneous iterative identification and restoration have been treated. The image and the noise have been modeled as multivariate Gaussian processes. Maximum-likelihood estimation has been used to estimate the parameters that characterize the Gaussian processes, where the estimation of the conditional mean of the image represents the restored image. Likelihood functions of observed images are highly nonlinear with respect to these parameters. Therefore, it is in general very difficult to maximize them directly. The expectation-maximization (EM) algorithm has been used to find these parameters. >
3 citations
23 May 1989
TL;DR: Mesh and mesh-of-pyramids implementations of iterative image restoration algorithms are proposed, based on a single-step algorithm as well as on a multistep iterative algorithm derived from the single step regularized iterative restoration algorithm.
Abstract: Mesh and mesh-of-pyramids implementations of iterative image restoration algorithms are proposed. These implementations are based on a single-step algorithm as well as on a multistep iterative algorithm derived from the single step regularized iterative restoration algorithm. One processor is assigned to each picture element, with local memory depending on the support of the restoration filter. The implementations consist of interprocessor communication and intraprocessor computations. The efficiency of the proposed VLSI algorithms is judged by establishing lower bounds on AT/sup 2/, where A is the area of the VLSI and T is its computation time. >
2 citations