The Experts below are selected from a list of 3483 Experts worldwide ranked by ideXlab platform
Andrey V. Nasonov - One of the best experts on this subject based on the ideXlab platform.
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ACIVS - Ringing Artifact Suppression Using Sparse Representation
Advanced Concepts for Intelligent Vision Systems, 2015Co-Authors: Alexey V. Umnov, Andrey S. Krylov, Andrey V. NasonovAbstract:The article refers to the problem of Ringing Artifact suppression. The Ringing effect is caused by high-frequency information corruption or loss, it appears as waves or oscillations near strong edges. We propose a novel method for Ringing Artifact suppression after Fourier cut-off filtering. It can be also used for image deRinging in the case of image resampling and other applications where the frequency loss can be estimated. The method is based on the joint sparse coding approach. The proposed method preserves more small image details than the state-of-the-art algorithms based on total variation minimization, and outperforms them in terms of image quality metrics.
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Ringing Artifact suppression using sparse representation
Advanced Concepts for Intelligent Vision Systems, 2015Co-Authors: Alexey V. Umnov, Andrey S. Krylov, Andrey V. NasonovAbstract:The article refers to the problem of Ringing Artifact suppression. The Ringing effect is caused by high-frequency information corruption or loss, it appears as waves or oscillations near strong edges. We propose a novel method for Ringing Artifact suppression after Fourier cut-off filtering. It can be also used for image deRinging in the case of image resampling and other applications where the frequency loss can be estimated. The method is based on the joint sparse coding approach. The proposed method preserves more small image details than the state-of-the-art algorithms based on total variation minimization, and outperforms them in terms of image quality metrics.
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Scale-space method of image Ringing estimation
2015Co-Authors: Andrey V. Nasonov, Andrey S. KrylovAbstract:Suppression of Ringing effect is a challenging problem. It is mainly caused by absence of effective methods of Ringing Artifact detection. In this paper we introduce a Ringing esti-mation method based on scale-space analysis. The estimation shows good results for low-pass filtered test images and in adaptive image deRinging. Index Terms — Ringing estimation, total variation, scale space, adaptive deringin
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Sparse method for Ringing Artifact detection
2014 12th International Conference on Signal Processing (ICSP), 2014Co-Authors: Alexey V. Umnov, Andrey S. Krylov, Andrey V. Nasonov, Ding YongAbstract:The problem of non-reference Ringing detection is considered. The idea of the proposed method is to decompose the input edges into the sums of blurred edge, Ringing oscillations and the residual using sparse representation approach with the pre-generated dictionary. Edges with Ringing effect are modeled by applying ideal low-pass filter to the step edge with different cut-off frequencies. Then they are separated into edge and Ringing components by subtracting the ideal step edge blurred with Gaussian filter. The shifted and rotated blur and Ringing components form the dictionary used for sparse representation. The presented method performs well on images with Ringing from different source types and is robust for the images with noise.
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Combined linear resampling method with Ringing control
2010Co-Authors: Andrey S. Krylov, Andrey V. Nasonov, Ra A. ChernomoretsAbstract:New method to combine different linear interpolation algorithms is suggested. It uses total variation analysis to suppress Ringing Artifact of the combination. This method enables to construct fast edge adaptive resampling methods. Its usage is illustrated with combinations of sinc, Papoulis and bicubic interpolation algorithms using new image metrics for interpolation methods quality analysis. The method can be also used to combine non-linear methods
D.l. Tull - One of the best experts on this subject based on the ideXlab platform.
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Maximum-likelihood parameter estimation for image Ringing-Artifact removal
'Institute of Electrical and Electronics Engineers (IEEE)', 2014Co-Authors: Yang Seungjoon, T.q. Nguyen, D.l. TullAbstract:At low bit rates, image compression codecs based on overlapping transforms introduce spurious oscillations known as Ringing Artifacts in the vicinity of major edges. Unlike previous works, we present a maximum-likelihood approach to the Ringing-Artifact removal problem. Our approach employs a parameter-estimation method based on the k-means algorithm with the number of clusters determined by a cluster-separation measure. The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images. Our results show effective and efficient removal of Ringing Artifacts.close405
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low bit rate video sequence coding Artifact removal
Multimedia Signal Processing, 2001Co-Authors: S Yang, Yu Hen Hu, S Kittitornkun, T.q. Nguyen, D.l. TullAbstract:The picture quality of video frames encoded at very low-bit rates often suffers from both blocking and Ringing Artifacts. We present two post-processing methods to mitigate the visual quality degradation caused by these Artifacts. To reduce the blocking Artifact of decoded images, we substitute IDCT for the lapped orthogonal transform embedded inverse discrete cosine transform (le-IDCT). On the other hand, we post-process the decoded video frames using a nonlinear robust filter to reduce the Ringing Artifact. Extensive simulation results indicated significant improvement in both objective and subjective visual qualities. The computation overhead incurred due to these quality enhancement operations is quite moderate, and can be easily optimized to achieve real-time operation.
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MMSP - Low bit rate video sequence coding Artifact removal
2001 IEEE Fourth Workshop on Multimedia Signal Processing (Cat. No.01TH8564), 2001Co-Authors: S Yang, Yu Hen Hu, S Kittitornkun, T.q. Nguyen, D.l. TullAbstract:The picture quality of video frames encoded at very low-bit rates often suffers from both blocking and Ringing Artifacts. We present two post-processing methods to mitigate the visual quality degradation caused by these Artifacts. To reduce the blocking Artifact of decoded images, we substitute IDCT for the lapped orthogonal transform embedded inverse discrete cosine transform (le-IDCT). On the other hand, we post-process the decoded video frames using a nonlinear robust filter to reduce the Ringing Artifact. Extensive simulation results indicated significant improvement in both objective and subjective visual qualities. The computation overhead incurred due to these quality enhancement operations is quite moderate, and can be easily optimized to achieve real-time operation.
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maximum likelihood parameter estimation for image Ringing Artifact removal
IEEE Transactions on Circuits and Systems for Video Technology, 2001Co-Authors: Seungjoon Yang, T.q. Nguyen, D.l. TullAbstract:At low bit rates, image compression codecs based on overlapping transforms introduce spurious oscillations known as Ringing Artifacts in the vicinity of major edges. Unlike previous works, we present a maximum-likelihood approach to the Ringing-Artifact removal problem. Our approach employs a parameter estimation method based on the k-means algorithm with the number of clusters determined by a cluster-separation measure. The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images. Our results show effective and efficient removal of Ringing Artifacts.
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maximum likelihood parameter estimation for image Ringing Artifact removal
International Conference on Image Processing, 2000Co-Authors: Seungjoon Yang, D.l. Tull, T.q. NguyenAbstract:At low bit rates, image compression codecs based on overlapping transforms introduce spurious oscillation known as Ringing Artifacts in the vicinity of major edges. The image quality can be enhanced considerably by removing the Artifacts. We present a maximum likelihood approach to the Ringing Artifact removal problem. Our approach employs a parameter estimation method based on the k-means algorithm with the number of clusters determined by a cluster separation measure. The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images to demonstrate their effectiveness.
Alexey V. Umnov - One of the best experts on this subject based on the ideXlab platform.
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ACIVS - Ringing Artifact Suppression Using Sparse Representation
Advanced Concepts for Intelligent Vision Systems, 2015Co-Authors: Alexey V. Umnov, Andrey S. Krylov, Andrey V. NasonovAbstract:The article refers to the problem of Ringing Artifact suppression. The Ringing effect is caused by high-frequency information corruption or loss, it appears as waves or oscillations near strong edges. We propose a novel method for Ringing Artifact suppression after Fourier cut-off filtering. It can be also used for image deRinging in the case of image resampling and other applications where the frequency loss can be estimated. The method is based on the joint sparse coding approach. The proposed method preserves more small image details than the state-of-the-art algorithms based on total variation minimization, and outperforms them in terms of image quality metrics.
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Ringing Artifact suppression using sparse representation
Advanced Concepts for Intelligent Vision Systems, 2015Co-Authors: Alexey V. Umnov, Andrey S. Krylov, Andrey V. NasonovAbstract:The article refers to the problem of Ringing Artifact suppression. The Ringing effect is caused by high-frequency information corruption or loss, it appears as waves or oscillations near strong edges. We propose a novel method for Ringing Artifact suppression after Fourier cut-off filtering. It can be also used for image deRinging in the case of image resampling and other applications where the frequency loss can be estimated. The method is based on the joint sparse coding approach. The proposed method preserves more small image details than the state-of-the-art algorithms based on total variation minimization, and outperforms them in terms of image quality metrics.
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Sparse method for Ringing Artifact detection
2014 12th International Conference on Signal Processing (ICSP), 2014Co-Authors: Alexey V. Umnov, Andrey S. Krylov, Andrey V. Nasonov, Ding YongAbstract:The problem of non-reference Ringing detection is considered. The idea of the proposed method is to decompose the input edges into the sums of blurred edge, Ringing oscillations and the residual using sparse representation approach with the pre-generated dictionary. Edges with Ringing effect are modeled by applying ideal low-pass filter to the step edge with different cut-off frequencies. Then they are separated into edge and Ringing components by subtracting the ideal step edge blurred with Gaussian filter. The shifted and rotated blur and Ringing components form the dictionary used for sparse representation. The presented method performs well on images with Ringing from different source types and is robust for the images with noise.
Marco Reisert - One of the best experts on this subject based on the ideXlab platform.
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gibbs Ringing Artifact removal based on local subvoxel shifts
Magnetic Resonance in Medicine, 2016Co-Authors: Elias Kellner, Bibek Dhital, Valerij G Kiselev, Marco ReisertAbstract:Purpose To develop a fast and stable method for correcting the gibbs-Ringing Artifact. Methods Gibbs-Ringing is a well-known Artifact which manifests itself as spurious oscillations in the vicinity of sharp image gradients at tissue boundaries. The origin can be seen in the truncation of k-space during MRI data-acquisition. Correction techniques like Gegenbauer reconstruction or extrapolation methods aim at recovering these missing data. Here, we present a simple and robust method which exploits a different view on the Gibbs-phenomenon: The truncation in k-space can be interpreted as a convolution of the underlying image with a sinc-function. As the image is reconstructed on a discretized grid, the severity of the Ringing Artifacts depends on how this grid is located with respect to the edge and the oscillation pattern of the function. We propose to reinterpolate the image based on local, subvoxel-shifts to sample the Ringing pattern at the zero-crossings of the oscillating sinc-function. Results With the proposed method, the Artifact can simply, effectively, and robustly be removed with a minimal amount of image smoothing. Conclusions The robustness of the method suggests it as a suitable candidate for an implementation in the standard image processing pipeline in clinical routine. Magn Reson Med 76:1574-1581, 2016. © 2015 International Society for Magnetic Resonance in Medicine.
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Gibbs-Ringing Artifact Removal Based on Local Subvoxel-shifts
arXiv: Medical Physics, 2015Co-Authors: Elias Kellner, Bibek Dhital, Marco ReisertAbstract:Gibbs-Ringing is a well known Artifact which manifests itself as spurious oscillations in the vicinity of sharp image transients, e.g. at tissue boundaries. The origin can be seen in the truncation of k-space during MRI data-acquisition. Consequently, correction techniques like Gegenbauer reconstruction or extrapolation methods aim at recovering these missing data. Here, we present a simple and robust method which exploits a different view on the Gibbs-phenomena. The truncation in k-space can be interpreted as a convolution with a sinc-function in image space. Hence, the severity of the Artifacts depends on how the sinc-function is sampled. We propose to re-interpolate the image based on local, subvoxel shifts to sample the Ringing pattern at the zero-crossings of the oscillating sinc-function. With this, the Artifact can effectively and robustly be removed with a minimal amount of smoothing.
Andrey S. Krylov - One of the best experts on this subject based on the ideXlab platform.
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ACIVS - Ringing Artifact Suppression Using Sparse Representation
Advanced Concepts for Intelligent Vision Systems, 2015Co-Authors: Alexey V. Umnov, Andrey S. Krylov, Andrey V. NasonovAbstract:The article refers to the problem of Ringing Artifact suppression. The Ringing effect is caused by high-frequency information corruption or loss, it appears as waves or oscillations near strong edges. We propose a novel method for Ringing Artifact suppression after Fourier cut-off filtering. It can be also used for image deRinging in the case of image resampling and other applications where the frequency loss can be estimated. The method is based on the joint sparse coding approach. The proposed method preserves more small image details than the state-of-the-art algorithms based on total variation minimization, and outperforms them in terms of image quality metrics.
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Ringing Artifact suppression using sparse representation
Advanced Concepts for Intelligent Vision Systems, 2015Co-Authors: Alexey V. Umnov, Andrey S. Krylov, Andrey V. NasonovAbstract:The article refers to the problem of Ringing Artifact suppression. The Ringing effect is caused by high-frequency information corruption or loss, it appears as waves or oscillations near strong edges. We propose a novel method for Ringing Artifact suppression after Fourier cut-off filtering. It can be also used for image deRinging in the case of image resampling and other applications where the frequency loss can be estimated. The method is based on the joint sparse coding approach. The proposed method preserves more small image details than the state-of-the-art algorithms based on total variation minimization, and outperforms them in terms of image quality metrics.
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Scale-space method of image Ringing estimation
2015Co-Authors: Andrey V. Nasonov, Andrey S. KrylovAbstract:Suppression of Ringing effect is a challenging problem. It is mainly caused by absence of effective methods of Ringing Artifact detection. In this paper we introduce a Ringing esti-mation method based on scale-space analysis. The estimation shows good results for low-pass filtered test images and in adaptive image deRinging. Index Terms — Ringing estimation, total variation, scale space, adaptive deringin
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Sparse method for Ringing Artifact detection
2014 12th International Conference on Signal Processing (ICSP), 2014Co-Authors: Alexey V. Umnov, Andrey S. Krylov, Andrey V. Nasonov, Ding YongAbstract:The problem of non-reference Ringing detection is considered. The idea of the proposed method is to decompose the input edges into the sums of blurred edge, Ringing oscillations and the residual using sparse representation approach with the pre-generated dictionary. Edges with Ringing effect are modeled by applying ideal low-pass filter to the step edge with different cut-off frequencies. Then they are separated into edge and Ringing components by subtracting the ideal step edge blurred with Gaussian filter. The shifted and rotated blur and Ringing components form the dictionary used for sparse representation. The presented method performs well on images with Ringing from different source types and is robust for the images with noise.
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Combined linear resampling method with Ringing control
2010Co-Authors: Andrey S. Krylov, Andrey V. Nasonov, Ra A. ChernomoretsAbstract:New method to combine different linear interpolation algorithms is suggested. It uses total variation analysis to suppress Ringing Artifact of the combination. This method enables to construct fast edge adaptive resampling methods. Its usage is illustrated with combinations of sinc, Papoulis and bicubic interpolation algorithms using new image metrics for interpolation methods quality analysis. The method can be also used to combine non-linear methods
