Results 231 to 240 of about 20,562 (267)

Robust Estimation of Motion Blur Kernel Using a Piecewise-Linear Model

IEEE Transactions on Image Processing, 2014
Blur 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
Gyeonghwan Kim
exaly   +3 more sources

Improved blur kernel estimation with blurred and noisy image pairs

2010 International Conference on Computer and Communication Technologies in Agriculture Engineering, 2010
In 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, null Yuan-Biao Zhang, null Chuan He, null Jia-Di Wan   +3 more
openaire   +1 more source

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
openaire   +2 more sources

Image deblurring with blur kernel estimation in RGB channels

2016 IEEE International Conference on Digital Signal Processing (DSP), 2016
Image 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, Hongqing Liu, Yong Li 0023, Yi Zhou 0014   +3 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

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
exaly   +2 more sources

Image Super-Resolution Reconstruction by Blur Kernel Estimation

2023 3rd International Conference on Communication Technology and Information Technology (ICCTIT), 2023
Xiaotong Liu, Hailong Jiang, Tong Xue, Chong Wang   +3 more
exaly   +2 more sources

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

Estimating Generalized Gaussian Blur Kernels for Out-of-Focus Image Deblurring

IEEE Transactions on Circuits and Systems for Video Technology, 2021
Out-of-focus blur is a common image degradation phenomenon that occurs in case of lens defocusing. The out-of-focus blur kernel is usually modeled as a Gaussian function or a uniform disk in previous work. In this paper, we propose that it can be more accurately depicted using the generalized Gaussian (GG) function. This is motivated by the theoretical
Yu-Qi Liu 0005, Xin Du 0005, Hui-Liang Shen, Shu-Jie Chen 0001   +3 more
openaire   +1 more source

Motion blur kernel estimation using noisy inertial data

2014 IEEE International Conference on Image Processing (ICIP), 2014
In 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