The Experts below are selected from a list of 34143 Experts worldwide ranked by ideXlab platform
Jiaya Jia - One of the best experts on this subject based on the ideXlab platform.
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two phase kernel estimation for robust motion deblurring
European Conference on Computer Vision, 2010Co-Authors: Li Xu, Jiaya JiaAbstract:We discuss a few new motion deblurring problems that are significant to kernel estimation and non-blind deconvolution. We found that strong edges do not always profit kernel estimation, but instead under certain circumstance degrade it. This finding leads to a new metric to measure the usefulness of image edges in motion deblurring and a Gradient Selection process to mitigate their possible adverse effect. We also propose an efficient and high-quality kernel estimation method based on using the spatial prior and the iterative support detection (ISD) kernel refinement, which avoids hard threshold of the kernel elements to enforce sparsity. We employ the TV-l1 deconvolution model, solved with a new variable substitution scheme to robustly suppress noise.
Xianfeng Gu - One of the best experts on this subject based on the ideXlab platform.
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kernel estimation from salient structure for robust motion deblurring
Signal Processing-image Communication, 2013Co-Authors: Zhixun Su, Xianfeng GuAbstract:Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good blur kernel from a single blurred image based on the image structure. We found that image details caused by blur could adversely affect the kernel estimation, especially when the blur kernel is large. One effective way to remove these details is to apply image denoising model based on the total variation (TV). First, we developed a novel method for computing image structures based on the TV model, such that the structures undermining the kernel estimation will be removed. Second, we applied a Gradient Selection method to mitigate the possible adverse effect of salient edges and improve the robustness of kernel estimation. Third, we proposed a novel kernel estimation method, which is capable of removing noise and preserving the continuity in the kernel. Finally, we developed an adaptive weighted spatial prior to preserve sharp edges in latent image restoration. Extensive experiments testify to the effectiveness of our method on various kinds of challenging examples.
Ernest D. Laue - One of the best experts on this subject based on the ideXlab platform.
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Pulsed-field Gradients: theory and practice.
Methods in Enzymology, 2004Co-Authors: James Keeler, Adrian L. V. Davis, Robin T. Clowes, Ernest D. LaueAbstract:Publisher Summary At the time of writing, pulsed-field Gradients are relative newcomers to the field of high-resolution NMR spectroscopy and have yet to be applied on even a semi-routine basis. Field Gradients do not offer unconditional improvements in the quality of spectra that can be obtained. In particular, attention has to be paid to the effects of Gradients on sensitivity and line shapes. The potential loss in sensitivity when using Gradient Selection, be it either inherent in the experiment or caused by deficiencies in the hardware used to generate the Gradients, is the most serious drawback of such experiments. However, there have already been demonstrated both striking improvements in the quality of spectra that can be obtained, as well as cases where considerable time savings can be made when sensitivity is not limiting . On the grounds of these alone, it is expected that Selection using pulsed-field Gradients will find a place in the repertoire of high-resolution NMR spectroscopists.
Li Xu - One of the best experts on this subject based on the ideXlab platform.
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two phase kernel estimation for robust motion deblurring
European Conference on Computer Vision, 2010Co-Authors: Li Xu, Jiaya JiaAbstract:We discuss a few new motion deblurring problems that are significant to kernel estimation and non-blind deconvolution. We found that strong edges do not always profit kernel estimation, but instead under certain circumstance degrade it. This finding leads to a new metric to measure the usefulness of image edges in motion deblurring and a Gradient Selection process to mitigate their possible adverse effect. We also propose an efficient and high-quality kernel estimation method based on using the spatial prior and the iterative support detection (ISD) kernel refinement, which avoids hard threshold of the kernel elements to enforce sparsity. We employ the TV-l1 deconvolution model, solved with a new variable substitution scheme to robustly suppress noise.
Zhixun Su - One of the best experts on this subject based on the ideXlab platform.
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kernel estimation from salient structure for robust motion deblurring
Signal Processing-image Communication, 2013Co-Authors: Zhixun Su, Xianfeng GuAbstract:Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good blur kernel from a single blurred image based on the image structure. We found that image details caused by blur could adversely affect the kernel estimation, especially when the blur kernel is large. One effective way to remove these details is to apply image denoising model based on the total variation (TV). First, we developed a novel method for computing image structures based on the TV model, such that the structures undermining the kernel estimation will be removed. Second, we applied a Gradient Selection method to mitigate the possible adverse effect of salient edges and improve the robustness of kernel estimation. Third, we proposed a novel kernel estimation method, which is capable of removing noise and preserving the continuity in the kernel. Finally, we developed an adaptive weighted spatial prior to preserve sharp edges in latent image restoration. Extensive experiments testify to the effectiveness of our method on various kinds of challenging examples.
