Results 211 to 220 of about 20,368 (248)
Some of the next articles are maybe not open access.
Space-variant blur kernel estimation and image deblurring through kernel clustering
Signal Processing: Image Communication, 2019Abstract 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.
Alam, Muhammad Zeshan +2 more
openaire +1 more source
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
openaire +2 more sources
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
openaire +1 more source
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
openaire +2 more sources
Robust motion blur kernel parameter estimation for star image deblurring
Optik, 2021Abstract Under dynamic conditions, the star images may be blurred and result in the decrease of attitude measurement accuracy of the star sensor. To estimate blur kernel parameters needed for star image deblurring, including blur angle and blur length, a method based on sparse representation, hyper-Laplacian priors, and ensemble neural network is ...
Xiyuan Chen +5 more
openaire +1 more source
Blur kernel estimation via salient edges and nonlocal regularization
2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015Blind image deblurring is a severely ill-posed inverse problem. To obtain a high quality latent image from a single blurred one, effective regularizations are required. In this paper, we propose a nonlocal regularization to improve blur kernel estimation.
Suil Son, Suk I. Yoo
openaire +1 more source
Image deblurring with blur kernel estimation in RGB channels
2016 IEEE International Conference on Digital Signal Processing (DSP), 2016Image deblurring aims to recover the clear image from the damaged image. The most existing blind image de-blurring approaches only consider estimating the blur kernel in the gray domain. In fact, for the color image produced by the digital camera, the blur effects for each color channel are usually different.
Xianqiu Xu +3 more
openaire +1 more source
Blur Kernel Optimization: A New Approach to Patch Selection with Adaptive Kernel Estimation
Applied Mechanics and Materials, 2013Recently, many effective approaches appeared in the field of blind image deconvolution to reduce the computational cost. Using multiple smaller regions instead of whole image not only make the restoration efficient but also improves the results by discarding the ineffectual regions. It is observed that a study is needed to compare different methods for
Saqib Yousaf, Shi Yin Qin
openaire +1 more source
Hybrid Regularized Blur Kernel Estimation for Single-Image Blind Deconvolution
2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015Single-image blind deconvolution is a challenging illposed inverse problem which requires regularization techniques to stabilize the restoration process. Its purpose is to recover an underlying blur kernel and a latent image from only one blurred image.
Ryan Wen Liu +3 more
openaire +1 more source
Spatial-scale-regularized blur kernel estimation for blind image deblurring
Signal Processing: Image Communication, 2018Abstract Blind 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 spatial scale sizes of image edges.
Shu Tang +5 more
openaire +1 more source