The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Aggelos K Katsaggelos - One of the best experts on this subject based on the ideXlab platform.
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generalized gaussian markov random field Image restoration using variational distribution approximation
International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: S D Babacan, Rafael Molina, Aggelos K KatsaggelosAbstract:In this paper we propose novel algorithms for Image restoration and parameter estimation with a generalized Gaussian Markov random field (GGMRF) prior utilizing variational distribution approximation. The Restored Image and the unknown hyperparameters for both the Image prior and the Image degradation noise are simultaneously estimated within a hierarchical Bayesian framework. We develop two algorithms resulting from this formulation which provide approximations to the posterior distributions of the latent variables. Experimental results are provided to demonstrate the performance of the algorithms.
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iterative regularized mixed norm multichannel Image restoration
Journal of Electronic Imaging, 2005Co-Authors: Min Cheol Hong, Tania Stathaki, Aggelos K KatsaggelosAbstract:We present a regularized mixed norm multichannel im- age restoration algorithm. The problem of multichannel restoration using both within- and between-channel deterministic information is considered. For each channel a functional that combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed. We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter that defines the degree of smoothness of the solution, both updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required, and the parameters just mentioned are adjusted based on the partially Restored Image. © 2005 SPIE and IS&T.
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a regularized iterative Image restoration algorithm
IEEE Transactions on Signal Processing, 1991Co-Authors: Aggelos K Katsaggelos, J Biemond, R W Schafer, Russell M. MersereauAbstract:The development of the algorithm is based on a set theoretic approach to regularization. Deterministic and/or statistical information about the undistorted Image and statistical information about the noise are directly incorporated into the iterative procedure. The Restored Image is the center of an ellipsoid bounding the intersection of two ellipsoids. The proposed algorithm, which has the constrained least squares algorithm as a special case, is extended into an adaptive iterative restoration algorithm. The spatial adaptivity is introduced to incorporate properties of the human visual system. Convergence of the proposed iterative algorithms is established. For the experimental results which are shown, the adaptively Restored Images have better quality than the nonadaptively Restored ones based on visual observations and on an objective criterion of merit which accounts for the noise masking property of the visual system. >
Duming Tsai - One of the best experts on this subject based on the ideXlab platform.
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an improved anisotropic diffusion model for detail and edge preserving smoothing
Pattern Recognition Letters, 2010Co-Authors: Shinmin Chao, Duming TsaiAbstract:It is important in Image restoration to remove noise while preserving meaningful details such as blurred thin edges and low-contrast fine features. The existing edge-preserving smoothing methods may inevitably take fine details as noise or vice versa. In this paper, we propose a new edge-preserving smoothing technique based on a modified anisotropic diffusion. The proposed method can simultaneously preserve edges and fine details while filtering out noise in the diffusion process. The classical anisotropic diffusion models consider only the gradient information of a diffused pixel, and cannot preserve detailed features with low gradient. Since the fine details in the neighborhood of the Image generally have larger gray-level variance than the noisy background, the proposed diffusion model incorporates both local gradient and gray-level variance to preserve edges and fine details while effectively removing noise. Experimental results from a variety of test samples including shoulder patch Images, medical Images and artwork Images have shown that the proposed anisotropic diffusion scheme can effectively smooth noisy background, yet well preserve edge and fine details in the Restored Image.
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automated surface inspection for statistical textures
Image and Vision Computing, 2003Co-Authors: Duming Tsai, Tseyun HuangAbstract:Abstract In this paper we present a global approach for the automatic inspection of defects in randomly textured surfaces which arise in sandpaper, castings, leather, and many industrial materials. The proposed method does not rely on local features of textures. It is based on a global Image reconstruction scheme using the Fourier transform (FT). Since a statistical texture has the surface of random pattern, the spread of frequency components in the power spectrum space is isotropic and forms the shape approximate to a circle. By finding an adequate radius in the spectrum space, and setting the frequency components outside the selected circle to zero, we can remove the periodic, repetitive patterns of any statistical textures using the inverse FT. In the Restored Image, the homogeneous region in the original Image will have an approximately uniform gray level, and yet the defective region will be distinctly preserved. This converts the difficult defect detection in textured Images into a simple thresholding problem in nontextured Images. The experimental results from a variety of real statistical textures have shown the efficacy of the proposed method.
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automated surface inspection for directional textures
Image and Vision Computing, 1999Co-Authors: Duming Tsai, C Y HsiehAbstract:Abstract In this paper we present a global approach for the automatic inspection of defects in directionally textured surfaces which arise in textile fabrics and machined surfaces. The proposed method does not rely on local features of textures. It is based on a global Image restoration scheme using the Fourier transform. The line patterns of any directional textures in the spatial domain Image are removed by detecting the high-energy frequency components in the Fourier domain Image using a one-dimensional (1D) Hough transform, setting them to zero, and finally back-transforming to a spatial domain Image. In the Restored Image, the homogeneous line region in the original Image will have an approximately uniform gray level, whereas the defective region will be distinctly preserved. A statistical process control scheme is therefore used to set up the control limits for discriminating between defects and homogeneous line patterns. The experiments on a variety of textile fabrics, machined surfaces and natural wood have shown the effectiveness of the proposed method.
S D Babacan - One of the best experts on this subject based on the ideXlab platform.
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generalized gaussian markov random field Image restoration using variational distribution approximation
International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: S D Babacan, Rafael Molina, Aggelos K KatsaggelosAbstract:In this paper we propose novel algorithms for Image restoration and parameter estimation with a generalized Gaussian Markov random field (GGMRF) prior utilizing variational distribution approximation. The Restored Image and the unknown hyperparameters for both the Image prior and the Image degradation noise are simultaneously estimated within a hierarchical Bayesian framework. We develop two algorithms resulting from this formulation which provide approximations to the posterior distributions of the latent variables. Experimental results are provided to demonstrate the performance of the algorithms.
Michael A Saunders - One of the best experts on this subject based on the ideXlab platform.
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variational bayesian Image restoration based on a product of t distributions Image prior
IEEE Transactions on Image Processing, 2008Co-Authors: G Chantas, Nikolaos Galatsanos, Aristidis Likas, Michael A SaundersAbstract:Image priors based on products have been recognized to offer many advantages because they allow simultaneous enforcement of multiple constraints. However, they are inconvenient for Bayesian inference because it is hard to find their normalization constant in closed form. In this paper, a new Bayesian algorithm is proposed for the Image restoration problem that bypasses this difficulty. An Image prior is defined by imposing Student-t densities on the outputs of local convolutional filters. A variational methodology, with a constrained expectation step, is used to infer the Restored Image. Numerical experiments are shown that compare this methodology to previous ones and demonstrate its advantages.
Russell M. Mersereau - One of the best experts on this subject based on the ideXlab platform.
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blur identification based on kurtosis minimization
International Conference on Image Processing, 2005Co-Authors: Dalong Li, Russell M. Mersereau, Steven J. SimskeAbstract:In this paper, we describe an algorithm for identifying a parametrically described blur based on kurtosis minimization. Using different choices for the parameters of the blur, the noisy blurred Image is Restored using Wiener filter. We use the kurtosis as a measurement of the quality of the Restored Image. From the set of the candidate deblurred Images, the one with the minimum kurtosis is selected. The proposed technique is tested in a simulated experiment on a variety of blurs including atmospheric turbulence blurs, Gaussian blurs, and out-of-focus blurs. The proposed approach is also tested on real blurred Images. Moreover, we test the performance when a wrong blur model is given. Our experiments show that the kurtosis minimization measurements match well with methods that maximize PSNR.
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a regularized iterative Image restoration algorithm
IEEE Transactions on Signal Processing, 1991Co-Authors: Aggelos K Katsaggelos, J Biemond, R W Schafer, Russell M. MersereauAbstract:The development of the algorithm is based on a set theoretic approach to regularization. Deterministic and/or statistical information about the undistorted Image and statistical information about the noise are directly incorporated into the iterative procedure. The Restored Image is the center of an ellipsoid bounding the intersection of two ellipsoids. The proposed algorithm, which has the constrained least squares algorithm as a special case, is extended into an adaptive iterative restoration algorithm. The spatial adaptivity is introduced to incorporate properties of the human visual system. Convergence of the proposed iterative algorithms is established. For the experimental results which are shown, the adaptively Restored Images have better quality than the nonadaptively Restored ones based on visual observations and on an objective criterion of merit which accounts for the noise masking property of the visual system. >
