Results 201 to 210 of about 20,368 (248)
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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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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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Blurred Image Restoration Using Fast Blur-Kernel Estimation
2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2014Motion blur is usually generated when people captured a picture in the daily life. This kind of blur is often non-liner motion and may cause the blurred contents seriously in this image. Hence, how to remove the blurred image into a clear image becomes a very important scheme.
Hui Yu Huang, Wei Chang Tsai
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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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Improved blur kernel estimation with blurred and noisy image pairs
2010 International Conference on Computer and Communication Technologies in Agriculture Engineering, 2010In this paper, we propose a TV-L1 denoising model-based kernel estimation in image deblurring which uses both blurred and noisy images. More details and edges are recovered in the denoised image which is used to replace the true image and do the deconvolution.
null Qian Wan +3 more
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Blur kernel estimation using the radon transform
CVPR 2011, 2011Camera shake is a common source of degradation in photographs. Restoring blurred pictures is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely underconstrained. In this work, we estimate camera shake by analyzing edges in the image, effectively constructing the Radon transform of the kernel ...
Taeg Sang Cho +3 more
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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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Blur kernel estimate in single noisy image deblurring
SPIE Proceedings, 2014Restoring blurred images is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely under constrained. Recently many single image blind deconvolution methods have been proposed, but these state-of-the-art single image deblurring techniques are still sensitive to image noise, and can degrade their ...
Shijie Sun, Huaici Zhao, Bo Li
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