Results 181 to 190 of about 293,019 (211)
Some of the next articles are maybe not open access.
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.
openaire +1 more source
Blind deblurring of spiral CT images
IEEE Transactions on Medical Imaging, 2003 The temporal bone is a complex paired set of struc-tures at the skull base which contains the organ of hear-ing among others [1]. Treatment of severe to profound, bilateral hearing loss often employs a multi-electrode cochlear implant inserted into the ...
Ge Wang, Michael Vannier, Ming Jiang
exaly +1 more source
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
openaire +2 more sources
A nonparametric procedure for blind image deblurring
Computational Statistics & Data Analysis, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
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
openaire +2 more sources
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
openaire +2 more sources
Unsupervised Blind Separation and Debluring of Mixtures of Sources
2007In this paper we consider the problem of separating source images from linear mixtures with unknown coefficients, in presence of noise and blur. In particular, we consider as a special case the problem of estimating the Cosmic Microwave Background from galactic and extra-galactic emissions.
L. Fedeli +2 more
openaire +2 more sources
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
openaire +1 more source
Blind Image Deblurring Based on Local Rank
Mobile Networks and Applications, 2019Conventional 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 +5 more
openaire +1 more source
Learning Data Terms for Non-blind Deblurring
2018Existing deblurring methods mainly focus on developing effective image priors and assume that blurred images contain insignificant amounts of noise. However, state-of-the-art deblurring methods do not perform well on real-world images degraded with significant noise or outliers. To address these issues, we show that it is critical to learn data fitting
Jiangxin Dong +4 more
openaire +1 more source