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Space-variant blur kernel estimation and image deblurring through kernel clustering
Signal Processing: Image Communication, 2019 Abstract This paper presents a space-variant blur kernel estimation and image deblurring framework. For space-variant blur kernel estimation, the input image is divided into small patches, and for each patch, the blur kernel is estimated. The estimated kernels are then grouped to determine different kernel clusters in the image.
M. Zeshan Alam +2 more
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Blur-Kernel Bound Estimation From Pyramid Statistics
IEEE Transactions on Circuits and Systems for Video Technology, 2016This letter presents an approach for automatically estimating the spatial bound of the blur kernel in a motion-blurred image based on the statistics of multilevel image gradients. We observe that blur has a significant impact on the histogram of oriented gradients (HOGs) at higher levels of an image pyramid, but has much less of an impact at coarser ...
Shaoguo Liu +3 more
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Joint blur kernel estimation and CNN for blind image restoration
Neurocomputing, 2020Abstract Convolutional neural networks (CNN) have shown its excellent performance in computer vision fields. Recently, they are successfully applied to image restoration. This paper proposes a joint blur kernel estimation and CNN method for blind image restoration. The blur kernel estimation is based on both blur support parameter estimation and blur
Liqing Huang, Youshen Xia
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Automatic blur-kernel-size estimation for motion deblurring
The Visual Computer, 2014Existing image deblurring approaches often take the blur-kernel-size as an important manual parameter. When set improperly, this parameter can lead to significant errors in the estimated blur kernels. However, manually specifying a proper kernel size for an input image is usually a tedious trial-and-error process.
Shaoguo Liu +4 more
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Space-varying blur kernel estimation and image deblurring
SPIE Proceedings, 2014 In recent years, we have seen highly successful blind image deblurring algorithms that can even handle large motion blurs. Most of these algorithms assume that the entire image is blurred with a single blur kernel. This assumption does not hold if the scene depth is not negligible or when there are multiple objects moving differently in the scene ...
Qinchun Qian, Bahadir K. Gunturk
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Parametric model for image blur kernel estimation
2018 International Conference on Orange Technologies (ICOT), 2018This paper we propose an novel parametric approach for single image kernel estimation with both motion blur and Gaussian blur coupled. In the view of that daily pictures captured by handheld device usually contain motion blur and defocus simultaneously.
Ao Zhang +4 more
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Robust Estimation of Motion Blur Kernel Using a Piecewise-Linear Model
IEEE Transactions on Image Processing, 2014Blur kernel estimation is a crucial step in the deblurring process for images. Estimation of the kernel, especially in the presence of noise, is easily perturbed, and the quality of the resulting deblurred images is hence degraded. Since every motion blur in a single exposure image can be represented by 2D parametric curves, we adopt a piecewise-linear
Sungchan Oh, Gyeonghwan Kim
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Image Super-Resolution Reconstruction by Blur Kernel Estimation
2023 3rd International Conference on Communication Technology and Information Technology (ICCTIT), 2023Xiaotong Liu +3 more
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Blur kernel estimation to improve recognition of blurred faces
2012 19th IEEE International Conference on Image Processing, 2012This paper proposes an efficient blind deconvolution method to deblur face images for face recognition. The method involves a salient edge map construction, blur kernel estimation and face image deconvolution. The combined Yale and Extended Yale face database B containing different illumination changes and blur conditions are used to evaluated the face
Chan, CH, Kittler, J
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