The Experts below are selected from a list of 166275 Experts worldwide ranked by ideXlab platform
M.k. Ng - One of the best experts on this subject based on the ideXlab platform.
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Super-Resolution Image Restoration from Blurred Low-Resolution Images
Journal of Mathematical Imaging and Vision, 2005Co-Authors: M.k. NgAbstract:In this paper, we study the problem of reconstructing a high-Resolution Image from several blurred low-Resolution Image frames. The Image frames consist of decimated, blurred and noisy versions of the high-Resolution Image. The high-Resolution Image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, the high-Resolution Image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore the high-Resolution Image. Computer simulations are given to illustrate the effectiveness of the proposed method.
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Super-Resolution Image restoration from blurred observations
2005 IEEE International Symposium on Circuits and Systems, 2005Co-Authors: N.k. Bose, M.k. NgAbstract:We study the problem of the reconstruction of a high-Resolution Image from several blurred low-Resolution Image frames. The Image frames consist of blurred, decimated and noisy versions of a high-Resolution Image. The high-Resolution Image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that, with the periodic boundary condition, a high-Resolution Image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore high-Resolution Images in the aperiodic boundary condition.
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ISCAS (6) - Super-Resolution Image restoration from blurred observations
2005 IEEE International Symposium on Circuits and Systems, 2005Co-Authors: N.k. Bose, M.k. NgAbstract:We study the problem of the reconstruction of a high-Resolution Image from several blurred low-Resolution Image frames. The Image frames consist of blurred, decimated and noisy versions of a high-Resolution Image. The high-Resolution Image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that, with the periodic boundary condition, a high-Resolution Image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore high-Resolution Images in the aperiodic boundary condition.
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High-Resolution Image reconstruction from rotated and translated low-Resolution Images with multisensors
International Journal of Imaging Systems and Technology, 2004Co-Authors: M.k. Ng, Wai-ki ChingAbstract:We extend the multisensor work by Bose and Boo (1998) and consider the perturbations of displacement error that are due to both translation and rotation. The warping process is introduced to obtain the ideal low-Resolution Image, which is located at exactly horizontal and vertical shift. In this approach, the problem of high-Resolution Image reconstruction is turned into the problem of Image restoration, and the system becomes spatially invariant rather than spatially variant in the original problem. An efficient algorithm is presented. Experimental results show that the proposed methods are quite effective, and they perform better than the bilinear Image interpolation method. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 75–83, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20010
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A Fast MAP Algorithm for High-Resolution Image Reconstruction with Multisensors
Multidimensional Systems and Signal Processing, 2001Co-Authors: M.k. NgAbstract:In many applications, it is required to reconstruct a high-Resolution Image from multiple, undersampled and shifted noisy Images. Using the regularization techniques such as the classical Tikhonov regularization and maximum a posteriori (MAP) procedure, a high-Resolution Image reconstruction algorithm is developed. Because of the blurring process, the boundary values of the low-Resolution Image are not completely determined by the original Image inside the scene. This paper addresses how to use (i) the Neumann boundary condition on the Image, i.e., we assume that the scene immediately outside is a reflection of the original scene at the boundary, and (ii) the preconditioned conjugate gradient method with cosine transform preconditioners to solve linear systems arising from the high-Resolution Image reconstruction with multisensors. The usefulness of the algorithm is demonstrated through simulated examples.
Aggelos K. Katsaggelos - One of the best experts on this subject based on the ideXlab platform.
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Regularized high-Resolution Image reconstruction considering inaccurate motion information
Optical Engineering, 2007Co-Authors: Min-kyu Park, Moon Gi Kang, Aggelos K. KatsaggelosAbstract:One of the major issues in recovering a high-Resolution Image from a sequence of low-Resolution observations is the accuracy of the motion information. In most of the work in the literature, the motion infor- mation is assumed to be known with high accuracy. This is very often not the case, and therefore the accuracy of high-Resolution Image recon- struction suffers substantially, since it greatly depends on the motion information. To address these issues in this paper, we propose a high- Resolution Image reconstruction algorithm that reduces the distortion in the reconstructed high-Resolution Image due to the inaccuracy of the estimated motion. Towards this task, we analyze the reconstruction noise generated by the inaccurate motion information. Based on this analysis, we propose a new regularization functional and derive a suffi- cient condition for the convergence of the resulting iterative reconstruc- tion algorithm. The proposed algorithm requires no prior information about the original Image or the inaccuracy of the motion information. Experimental results illustrate the benefit of the proposed method when compared to conventional high-Resolution Image reconstruction methods in terms of both objective measurements and subjective evaluation.
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Parameter estimation in super-Resolution Image reconstruction problems
2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP '03)., 2003Co-Authors: J. Abad, M. Vega, R. Molina, Aggelos K. KatsaggelosAbstract:We consider the estimation of the unknown hyperparameters for the problem of reconstructing a high-Resolution Image from multiple undersampled, shifted, degraded frames with subpixel displacement errors. We derive mathematical expressions for the iterative calculation of the maximum likelihood estimate (MLE) of the unknown hyperparameters given the low Resolution observed Images. Experimental results are presented for evaluating the accuracy of the proposed method.
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Reconstruction of a High Resolution Image from Multiple Low Resolution Images
The International Series in Engineering and Computer Science, 2002Co-Authors: Nikolas P. Galatsanos, Aggelos K. KatsaggelosAbstract:In this chapter the problem of reconstructing a high Resolution Image from multiple aliased and shifted by sub-pixel shifts low Resolution Images is considered. The low Resolution Images are possibly degraded by unknown blurs and their sub-pixel shifts are not known. This problem is described in the frequency and spatial domains. Algorithms for providing solutions to it are reviewed. In addition, two approaches are presented in detail for solving this low-to-high Resolution problem. In the first of these two approaches registration and restoration is performed simultaneously using the expectation-maximization (EM) algorithm. The high Resolution Image is then reconstructed using regularized interpolation which is performed as a separate step. For this reason this approach is abbreviated as RR-I which corresponds to registration/restorationinterpolation. In the second of these approaches registration, restoration and interpolation are perfomed simultaneously using the EM algorithm. Therefore this approach is abbreviated as RRI which corresponds to registration/restoration/interpolation. Numerical experiments are presented that demonstrate the effectiveness of the two approaches.
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ICIP - Reconstruction of a high-Resolution Image by simultaneous registration, restoration, and interpolation of low-Resolution Images
Proceedings. International Conference on Image Processing, 1995Co-Authors: Aggelos K. KatsaggelosAbstract:In this paper a solution is provided to the problem of obtaining a high Resolution Image from several low Resolution Images that have been subsampled and displaced by different amounts of sub-pixel shifts. In its most general form, this problem can be broken up into three sub-problems: registration, restoration, and interpolation. Previous work has either solved all three sub-problems independently, or more recently, solved either the first two steps (registration and restoration) or the last two steps together. However, none of the existing methods solve all three sub-problems simultaneously. This paper poses the low Resolution to high Resolution problem as a maximum likelihood (ML) problem which is solved by the expectation-maximization (EM) algorithm. By exploiting the structure of the matrices involved, the problem ran be solved in the discrete frequency domain. The ML problem is then the estimation of the sub-pixel shifts, the noise variances of each Image, the power spectra of the high Resolution Image, and the high Resolution Image itself. Experimental results are shown which demonstrate the effectiveness of this approach.
M.i. Sezan - One of the best experts on this subject based on the ideXlab platform.
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High-Resolution Image reconstruction from lower-Resolution Image sequences and space-varying Image restoration
[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics Speech and Signal Processing, 1992Co-Authors: A.m. Tekalp, M.k. Ozkan, M.i. SezanAbstract:The authors address the problem of reconstruction of a high-Resolution Image from a number of lower-Resolution (possibly noisy) frames of the same scene where the successive frames are uniformly based versions of each other at subpixel displacements. In particular, two previously proposed methods, a frequency-domain method and a method based on projections onto convex sets (POCSs), are extended to take into account the presence of both sensor blurring and observation noise. A new two-step procedure is proposed, and it is shown that the POCS formulation presented for the high-Resolution Image reconstruction problem can also be used as a new method for the restoration of spatially invariant blurred Images. Some simulation results are provided.
N.k. Bose - One of the best experts on this subject based on the ideXlab platform.
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Super-Resolution Image restoration from blurred observations
2005 IEEE International Symposium on Circuits and Systems, 2005Co-Authors: N.k. Bose, M.k. NgAbstract:We study the problem of the reconstruction of a high-Resolution Image from several blurred low-Resolution Image frames. The Image frames consist of blurred, decimated and noisy versions of a high-Resolution Image. The high-Resolution Image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that, with the periodic boundary condition, a high-Resolution Image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore high-Resolution Images in the aperiodic boundary condition.
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ISCAS (6) - Super-Resolution Image restoration from blurred observations
2005 IEEE International Symposium on Circuits and Systems, 2005Co-Authors: N.k. Bose, M.k. NgAbstract:We study the problem of the reconstruction of a high-Resolution Image from several blurred low-Resolution Image frames. The Image frames consist of blurred, decimated and noisy versions of a high-Resolution Image. The high-Resolution Image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that, with the periodic boundary condition, a high-Resolution Image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore high-Resolution Images in the aperiodic boundary condition.
Zhang Liangpei - One of the best experts on this subject based on the ideXlab platform.
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Super Resolution Image Reconstruction Applied in Remote Sensing
Geospatial Information, 2007Co-Authors: Zhang LiangpeiAbstract:Super Resolution (SR) Image reconstruction is a technique to recover a high Resolution Image from several low Resolution Images using the additional information among them. In this paper,we introduce the applic-ations of SR techniques to remote sensing observing systems. We also give a SR method to multi-temporal remote sensing Images.
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A Regularized Super-Resolution Image Reconstruction Method
Journal of Image and Graphics, 2005Co-Authors: Zhang LiangpeiAbstract:Super-Resolution Image reconstruction has been one of the most active research areas in recent years. In this paper, a super-Resolution solution is proposed to the problem of obtaining a high Resolution Image from several low Resolution Images that have been subsampled and displaced by different amounts of sub-pixel shifts. The method is based on the regularization technique, solving the constrained optimization by proposed iteration steps. At each iteration step, the regularization parameter is determined using the partially reconstructed Image solved at the last step. The proposed algorithm is tested on synthetic Images,and the reconstructed Images are evaluated by a PSNR method. The results indicate that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.
