The Experts below are selected from a list of 16563 Experts worldwide ranked by ideXlab platform
Lijuan Zhang - One of the best experts on this subject based on the ideXlab platform.
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robust multi frame adaptive optics image Restoration Algorithm using maximum likelihood estimation with poisson statistics
Sensors, 2017Co-Authors: Dongming Li, Jinhua Yang, Jiaqi Peng, Lijuan ZhangAbstract:An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image Restoration Algorithm via maximum likelihood estimation. Our proposed Algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution Algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image Restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed Algorithm. Experimental results show that our Algorithm produces accurate AO image Restoration results and outperforms the current state-of-the-art blind deconvolution methods.
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robust multi frame adaptive optics image Restoration Algorithm using maximum likelihood estimation with poisson statistics
Sensors, 2017Co-Authors: Changming Sun, Jinhua Yang, Jiaqi Peng, Lijuan Zhang, Huan LiuAbstract:An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image Restoration Algorithm via maximum likelihood estimation. Our proposed Algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution Algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image Restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed Algorithm. Experimental results show that our Algorithm produces accurate AO image Restoration results and outperforms the current state-of-the-art blind deconvolution methods.
Aggelos K Katsaggelos - One of the best experts on this subject based on the ideXlab platform.
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a novel iterative image Restoration Algorithm using nonstationary image priors
International Conference on Image Processing, 2011Co-Authors: Esteban Vera, Rafael Molina, Miguel Vega, Aggelos K KatsaggelosAbstract:In this paper, we propose a novel Algorithm for image Restoration based on combining nonstationary edge-preserving priors. We develop a Bayesian modeling followed by an evidence analysis inference approach for deriving the foundations of the proposed iterative Restoration Algorithm. Simulation results over a variety of blurred and noisy standard test images indicate that the presented method outperforms current state-of-the-art image Restoration Algorithms. We finally present experimental results by digitally refocusing images captured with controlled defocus, successfully confirming the ability of the proposed Restoration Algorithm in recovering extra features and details, while still preserving edges.
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variational bayesian image Restoration with a product of spatially weighted total variation image priors
IEEE Transactions on Image Processing, 2010Co-Authors: Giannis Chantas, Rafael Molina, N P Galatsanos, Aggelos K KatsaggelosAbstract:In this paper, a new image prior is introduced and used in image Restoration. This prior is based on products of spatially weighted total variations (TV). These spatial weights provide this prior with the flexibility to better capture local image features than previous TV based priors. Bayesian inference is used for image Restoration with this prior via the variational approximation. The proposed Restoration Algorithm is fully automatic in the sense that all necessary parameters are estimated from the data and is faster than previous similar Algorithms. Numerical experiments are shown which demonstrate that image Restoration based on this prior compares favorably with previous state-of-the-art Restoration 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 vq based blind image Restoration Algorithm
IEEE Transactions on Image Processing, 2003Co-Authors: R Nakagaki, Aggelos K KatsaggelosAbstract:Learning-based Algorithms for image Restoration and blind image Restoration are proposed. Such Algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (Restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During Restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind Restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the Restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed Algorithms.
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simultaneous iterative image Restoration and evaluation of the regularization parameter
IEEE Transactions on Signal Processing, 1992Co-Authors: Moon Gi Kang, Aggelos K KatsaggelosAbstract:A nonlinear regularized iterative image Restoration Algorithm is proposed, according to which only the noise variance is assumed to be known in advance. The Algorithm results from a set theoretic regularization approach, where a bound of the stabilizing functional, and therefore the regularization parameter, are updated at each iteration step. Sufficient conditions for the convergence of the Algorithm are derived and experimental results are shown. >
Jiaqi Peng - One of the best experts on this subject based on the ideXlab platform.
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robust multi frame adaptive optics image Restoration Algorithm using maximum likelihood estimation with poisson statistics
Sensors, 2017Co-Authors: Dongming Li, Jinhua Yang, Jiaqi Peng, Lijuan ZhangAbstract:An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image Restoration Algorithm via maximum likelihood estimation. Our proposed Algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution Algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image Restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed Algorithm. Experimental results show that our Algorithm produces accurate AO image Restoration results and outperforms the current state-of-the-art blind deconvolution methods.
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robust multi frame adaptive optics image Restoration Algorithm using maximum likelihood estimation with poisson statistics
Sensors, 2017Co-Authors: Changming Sun, Jinhua Yang, Jiaqi Peng, Lijuan Zhang, Huan LiuAbstract:An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image Restoration Algorithm via maximum likelihood estimation. Our proposed Algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution Algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image Restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed Algorithm. Experimental results show that our Algorithm produces accurate AO image Restoration results and outperforms the current state-of-the-art blind deconvolution methods.
Jinhua Yang - One of the best experts on this subject based on the ideXlab platform.
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robust multi frame adaptive optics image Restoration Algorithm using maximum likelihood estimation with poisson statistics
Sensors, 2017Co-Authors: Dongming Li, Jinhua Yang, Jiaqi Peng, Lijuan ZhangAbstract:An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image Restoration Algorithm via maximum likelihood estimation. Our proposed Algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution Algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image Restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed Algorithm. Experimental results show that our Algorithm produces accurate AO image Restoration results and outperforms the current state-of-the-art blind deconvolution methods.
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robust multi frame adaptive optics image Restoration Algorithm using maximum likelihood estimation with poisson statistics
Sensors, 2017Co-Authors: Changming Sun, Jinhua Yang, Jiaqi Peng, Lijuan Zhang, Huan LiuAbstract:An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image Restoration Algorithm via maximum likelihood estimation. Our proposed Algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution Algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image Restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed Algorithm. Experimental results show that our Algorithm produces accurate AO image Restoration results and outperforms the current state-of-the-art blind deconvolution methods.
Dongming Li - One of the best experts on this subject based on the ideXlab platform.
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robust multi frame adaptive optics image Restoration Algorithm using maximum likelihood estimation with poisson statistics
Sensors, 2017Co-Authors: Dongming Li, Jinhua Yang, Jiaqi Peng, Lijuan ZhangAbstract:An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image Restoration Algorithm via maximum likelihood estimation. Our proposed Algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution Algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image Restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed Algorithm. Experimental results show that our Algorithm produces accurate AO image Restoration results and outperforms the current state-of-the-art blind deconvolution methods.
