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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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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.
null Haichao Zhang +3 more
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Quality measures for blind image deblurring
2012 IEEE International Conference on Imaging Systems and Techniques Proceedings, 2012Blind image deblurring is limited by the unavailability or in many cases little information about the PSF. If the PSF is estimated, then deblurring simplifies to just deconvolving the blurred image with the PSF using any conventional deblurring filter. We have recently proposed a blind deblurring scheme using kurtosis measures.
Aftab Khan, Hujun Yin
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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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An Efficient Blind Image Deblurring Algorithm
Key Engineering Materials, 2010This paper presents a novel algorithm which concerns with the fast implement of blind image deblurring with a well-reconstructed original image. Firstly, we model both the original image and the blur utilizing the harmonic model in the Sobolev image space, based on which, the prior distributions of them are obtained; Secondly, the Gamma distribution is
Su Xiao +3 more
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Blind motion deblurring using multiple images
Journal of Computational Physics, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cai, Jian-Feng +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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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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Blind Image Deblurring Using Adaptive Priors
2018For blind image deblurring, a good prior knowledge can guide the maximum a posterior (MAP) based algorithms to be away from the trivial solution. Therefore, many existing methods focus on designing effective priors to constrain the solution space. However, blind deconvolution with fixed priors is not robust.
Bingwang Zhang +5 more
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Blind Deblurring Using Discriminative Image Smoothing
2018This paper aims to exploit the full potential of gradient-based methods, attempting to explore a simple, robust yet discriminative image prior for blind deblurring. The specific contributions are three-fold: Above all, a pure gradient-based heavy-tailed model is proposed as a generalized integration of the normalized sparsity and the relative total ...
Wenze Shao +5 more
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