Results 161 to 170 of about 4,574 (198)
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Non-uniform Blind Deblurring by Reblurring
2017 IEEE International Conference on Computer Vision (ICCV), 2017We present an approach for blind image deblurring, which handles non-uniform blurs. Our algorithm has two main components: (i) A new method for recovering the unknown blur-field directly from the blurry image, and (ii) A method for deblurring the image given the recovered non-uniform blur-field.
Yuval Bahat, Netalee Efrat, Michal Irani
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Smoothing Priors for Blind Image Deblurring
SIAM Journal on Imaging ScienceszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Haobo Xu, Fang Li 0004
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Blind image deblurring by game theory
Proceedings of the 2nd International Conference on Networking, Information Systems & Security, 2019In this paper, we present a novel blind deconvolution technique for the restoration of linearly degraded images without explicit knowledge of either the original image or the point spread function (PSF). We propose to determine the optimal image deblurring as a Nash equilibrium, we use two criteria associated with two players.
Driss Meskine +2 more
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An Energy-Scalable Accelerator for Blind Image Deblurring
IEEE Journal of Solid-State Circuits, 2016Camera shake is the leading cause of blur in cell-phone camera images. Removing blur requires deconvolving the blurred image with a kernel which is typically unknown and needs to be estimated from the blurred image. This kernel estimation is computationally intensive and takes several minutes on a CPU which makes it unsuitable for mobile devices.
Priyanka Raina +2 more
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Enhanced Sparse Model for Blind Deblurring
2020Existing arts have shown promising efforts to deal with the blind deblurring task. However, most of the recent works assume the additive noise involved in the blurring process to be simple-distributed (i.e. Gaussian or Laplacian), while the real-world case is proved to be much more complicated.
Liang Chen +4 more
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Blind Deblurring and Deconvolution
Signal Recovery and Synthesis, 1995Many imaging systems in use today acquire data that are related to a desired object function f(·) through the linear relationship where h(·,·; θ) is the point-spread function for the imaging system, and θ is a collection of system parameters – some or all of which may be unknown – that characterize the system and, hence, its point-spread function.
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Blind image deblurring with reinforced use of edges
The Visual Computer, 2019Blind image deblurring tries to restore a blurred image to a clear image without the blurring kernel known in advance, which is widely required in applications such as computer vision and medical image processing. With regard to this, the key issues here are to accurately estimate the blurring kernel for deconvolution of a blurred image, and avoid the ...
Qiu Feng +2 more
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A nonparametric procedure for blind image deblurring
Computational Statistics & Data Analysis, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A Comparative Study for Single Image Blind Deblurring
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016Numerous single image blind deblurring algorithms have been proposed to restore latent sharp images under camera motion. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real blurred images. It is thus unclear how these algorithms would perform on images acquired "in the wild" and how we could gauge the ...
Wei-Sheng Lai +4 more
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Blur-Invariant Deep Learning for Blind-Deblurring
2017 IEEE International Conference on Computer Vision (ICCV), 2017In this paper, we investigate deep neural networks for blind motion deblurring. Instead of regressing for the motion blur kernel and performing non-blind deblurring outside of the network (as most methods do), we propose a compact and elegant end-to-end deblurring network.
Thekke Madam Nimisha +2 more
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