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Blind Image Deblurring with Outlier Handling
2017 IEEE International Conference on Computer Vision (ICCV), 2017Deblurring images with outliers has attracted considerable attention recently. However, existing algorithms usually involve complex operations which increase the difficulty of blur kernel estimation. In this paper, we propose a simple yet effective blind image deblurring algorithm to handle blurred images with outliers. The proposed method is motivated
Jiangxin Dong +3 more
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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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Single image blind deblurring with image decomposition
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012How to deal with themotion blurred image is a common problem in our daily life. Restoring blurred images is challenging, especially when both the blur kernel and the sharp image are unknown. In this work, we present a new algorithm for removing motion blur from a single image, which incorporates the image decomposition into the image deblurring process.
Yuquan Xu +3 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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Non-blind Image Deblurring from a Single Image
Cognitive Computation, 2012Conventional non-blind image deblurring algorithms often involve in maximum a posteriori (MAP) estimation and natural image priors. However, MAP estimation has several disadvantages which limit its application. To address these issues, we propose to use Bayesian minimum mean squared error (MMSE) estimation instead of MAP to perform deblurring.
Bo Zhao +3 more
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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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Blind deblurring using adaptive image model
2016 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2016The paper studies the blind deblurring algorithms using the proposed adaptive image model which is based on the random line field. Both algorithms are constructed following the Bayesian framework. The deblurring results of the proposed algorithm are compared with those of the deblurring algorithms in the literature.
Ngoc-Thuy Le, Ngoc-Minh Nguyen
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Deblur-CycleGAN: A Generative Cyclic Approach for Image Blind Motion Deblurring
2022 7th International Conference on Computer and Communication Systems (ICCCS), 2022In this paper, we propose an end-to-end generative adversarial network (GAN) for single image blind motion deblur-ring, which we called Deblur-CycleGAN. Inspired by the cyclic nature of the original CycleGAN, we perform single image blind motion deblurring in similar fashion while presenting motion deblurring as a cycle-consistent approach.
Saqlain, Ali Syed +4 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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Sparse representation based blind image deblurring
2011 IEEE International Conference on Multimedia and Expo, 2011We propose a sparse representation based blind image deblurring method. The proposed method exploits the sparsity property of natural images, by assuming that the patches from the natural images can be sparsely represented by an over-complete dictionary.
Haichao Zhang 0001 +3 more
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