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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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Kernel-Based Motion-Blurred Target Tracking
2011Motion blurs are pervasive in real captured video data, especially for hand-held cameras and smartphone cameras because of their low frame rate and material quality. This paper presents a novel Kernel-based motion-Blurred target Tracking (KBT) approach to accurately locate objects in motion blurred video sequence, without explicitly performing ...
Yi Wu +5 more
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Spatial-scale-regularized blur kernel estimation for blind image deblurring
Signal processing. Image communication, 2018Blind image deblurring is a long-standing and challenging inverse problem in image processing. In this paper, we propose a new spatial-scale-regularized approach to estimate a blur kernel (BK) from a single motion blurred image by regularizing the ...
Shu Tang +5 more
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Motion-blur kernel size estimation via learning a convolutional neural network
Pattern Recognition Letters, 2017Deblurring is to restore a latent clear image as well as to estimate an underlying blur kernel from a single blurry image. Motion blur kernel size is a significant input parameter of existing deblurring algorithms.
Lerenhan Li +3 more
semanticscholar +1 more source
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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Blur kernel re-initialization for blind image deblurring
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016We propose a simple yet effective blur kernel re-initialization method in a coarse-to-fine framework for blind image deblurring. The proposed method is motivated by observing that most deblurring algorithms use only an estimated blur kernel at the coarser level to initialize a blur kernel for the next finer level.
Hyukzae Lee, Changick Kim
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Space-varying blur kernel estimation and image deblurring
SPIE Proceedings, 2014In 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 ...
Qian, Qinchun +1 more
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Motion blur kernel estimation using noisy inertial data
2014 IEEE International Conference on Image Processing (ICIP), 2014In the case of motion blur due to unknown motion, most of the existing image deblurring algorithms rely on good initial estimate of the kernel or latent image obtained through blind deconvolution and only consider 3-dimensional camera motions. To overcome these problems, Joshi [1] presented a novel blur kernel estimation and image deblurring approach ...
Ruiwen Zhen, Robert L. Stevenson
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Method to detect and calculate motion blur kernel
SPIE Proceedings, 2010Motion during camera's exposure time causes image blur, we call it motion blur. According to the linear system theory, if we can find the blur kernel which has the same meaning of point spread function, the blurred image can be restored by the blur kernel using iterative algorithms, such as R-L (Richardson-Lucy).
Jiagu Wu +4 more
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Camera intrinsic blur kernel estimation: A reliable framework
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015This paper presents a reliable non-blind method to measure intrinsic lens blur. We first introduce an accurate camera-scene alignment framework that avoids erroneous homography estimation and camera tone curve estimation. This alignment is used to generate a sharp correspondence of a target pattern captured by the camera.
Ali Mosleh +4 more
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