Results 171 to 180 of about 4,369 (201)

Blind Image Deblurring Based on Local Rank

Mobile Networks and Applications, 2019
Conventional algorithms for blind image deblurring are often inaccurate at blur kernel estimation, and the recovery effect is far from perfect. To address this, we propose a single-image blind deblurring method based on local rank. For this, we first impose adaptive threshold segmentation on a conventional local rank transform, which is subsequently ...
Li Zhu 0003, Long Jin, Jihua Zhu, Zhongyu Li 0002, Zhiqiang Tian, Huimin Lu 0001   +5 more
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A nonparametric procedure for blind image deblurring

Computational Statistics & Data Analysis, 2008
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Sparse representation based blind image deblurring

2011 IEEE International Conference on Multimedia and Expo, 2011
We 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, Jianchao Yang, Yanning Zhang 0001, Thomas S. Huang   +3 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), 2016
The 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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Example-Guided Image Prior for Blind Image Deblurring

2017
This paper proposes a patch-based deblurring method to leverage the unregistered sharp example image which shares global or local contents with the blurred image in a variant view. Firstly, we propose a coarse-to-fine scheme to achieve the accurate image patches matching and solve the mismatch problem caused by the blur ambiguity.
Xueling Chen, Yu Zhu 0004, Wei Sun 0036, Yanning Zhang 0001   +3 more
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Blind Image Deblurring Based on Dictionary Replacing

2012
Traditional image deblurring is based on deconvolution, an ill-posed problem, which is sensitive to the accuracy of the blur kernel. In this paper, we propose a blind image deblurring method based on dictionary replacing. First, we estimate the blur kernel from the blur image , and then based on the sparse representation of the image patch under over ...
Haisen Li, Yanning Zhang 0001, Feng Duan, Yu Zhu 0004   +3 more
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Blind Deblurring Using Discriminative Image Smoothing

2018
This 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, Yunzhi Lin, Bingkun Bao, Liqian Wang, Qi Ge, Haibo Li 0001   +5 more
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Blind image deblurring by promoting group sparsity

Neurocomputing, 2018
Abstract Blind image deblurring aims to recover the sharp image from a blurred observation, which is an ill-posed inverse problem. Proper image priors for the unknown variables (i.e. latent sharp image and blur kernel) are crucial. Abundant previous methods have shown the effectiveness of the sparsity-based priors on both image gradients and the blur
Dong Gong, Rui Li 0013, Yu Zhu 0004, Haisen Li, Jinqiu Sun, Yanning Zhang 0001   +5 more
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Blind Image Deblurring Using Adaptive Priors

2018
For 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, Risheng Liu, Haojie Li, Qi Yuan, Xin Fan 0001, Zhongxuan Luo   +5 more
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Class-Adapted Blind Deblurring of Document Images

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017
Deblurring of document images is an important problem, with several relevant applications, such as camera-based document acquisition and processing systems. Consequently, considerable attention has been given to this problem, namely in the blind image deblurring (BID) scenario, where the blurring filter is (partially or fully) unknown.
Ljubenovic, Marina, Zhuang, Lina, Figueiredo, Mario A.T.   +2 more
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