Results 241 to 250 of about 36,154 (265)

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, Qinchun Qian, Bahadir K. Gunturk   +2 more
openaire   +1 more source

Camera intrinsic blur kernel estimation: A reliable framework

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
This 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 0002, Paul Green 0002, Emmanuel Onzon, Isabelle Bégin, J. M. Pierre Langlois   +4 more
openaire   +2 more sources

Blur kernel re-initialization for blind image deblurring

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016
We 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
exaly   +2 more sources

Method to detect and calculate motion blur kernel

SPIE Proceedings, 2010
Motion 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, Huajun Feng, Zhihai Xu, Qi Li, Zhongliang Fu   +4 more
openaire   +1 more source

Parametric model for image blur kernel estimation

2018 International Conference on Orange Technologies (ICOT), 2018
This 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, Yu Zhu, Jinqiu Sun, Min Wang, Yanning Zhang   +4 more
openaire   +1 more source

Mixture of Gaussian Blur Kernel Representation for Blind Image Restoration

IEEE Transactions on Computational Imaging, 2017
Blind image restoration is a nonconvex problem involving the restoration of images using unknown blur kernels. The success of the restoration process depends on three factors: first, the amount of prior information concerning the image and blur kernel; second, the algorithm used to perform restoration; and third, the initial guesses made by the ...
Chia-Chen Lee, Wen-Liang Hwang
openaire   +1 more source

Designing the optimal convolution kernel for modeling the motion blur

SPIE Proceedings, 2011
Motion blur acts on an image like a two dimensional low pass filter, whose spatial frequency characteristic depends both on the trajectory of the relative motion between the scene and the camera and on the velocity vector variation along it. When motion during exposure is permitted, the conventional, static notions of both the image exposure and the ...
openaire   +1 more source

Blur Kernel Estimation

2021
openaire   +1 more source

A Nonblind Deconvolution Method by Bias Correction for Inaccurate Blur Kernel Estimation in Image Deblurring

IEEE Transactions on Geoscience and Remote Sensing, 2023
Jie Han, Zhen Ye
exaly  

Joint blur kernel estimation and CNN for blind image restoration

Neurocomputing, 2020
Liqing Huang, Youshen Xia
exaly