Results 251 to 260 of about 512,229 (281)

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.
, Yu Zhu, Yanning Zhang
exaly   +2 more sources

Blur kernel estimation to improve recognition of blurred faces

2012 19th IEEE International Conference on Image Processing, 2012
This 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
openaire   +4 more sources

Blurred Image Restoration Using Fast Blur-Kernel Estimation

2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2014
Motion 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
openaire   +1 more source

Blur kernel estimation using the radon transform

CVPR 2011, 2011
Camera 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, Sylvain Paris, Berthold K. P. Horn, William T. Freeman   +3 more
openaire   +1 more source

A Learnable Blur Kernel for Remote Sensing Image Retrieval

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
With the explosive increase of remote sensing images, content-based remote sensing image retrieval (CBRSIR) has aroused widespread attention. Convolutional Neural Network (CNN) based methods are widely used in CBRSIR due to the development of deep learning. However, common used CNN models have difficulties in holding shift-invariant property due to the
Zelin Peng, Guanchun Wang, Xiangrong Zhang, Xu Tang 0004, Li Gao, Licheng Jiao   +5 more
openaire   +2 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

Joint Image Registration and Blur Kernel Learning for Pansharpening

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
Image registration and the estimation of spatial and spectral blur kernels are essential steps before fusing panchromatic image (PAN) and multispectral image (MSI). Usually, these basic steps are performed separately, which will lead to error accumulation and ultimately affect the fusion performance. In this paper, we propose a novel deep learning (DL)
Anjing Guo, Yue Wu 0007, Shutao Li 0001
openaire   +2 more sources

Kernel-Based Motion-Blurred Target Tracking

2011
Motion 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 0001, Jing Hu, Feng Li 0005, Erkang Cheng, Jingyi Yu 0001, Haibin Ling   +5 more
openaire   +1 more source

Multiframe image restoration in the presence of noisy blur kernel

2009 16th IEEE International Conference on Image Processing (ICIP), 2009
We wish to recover an original image u from several blurry-noisy versions f k , called frames. We assume a more severe degradation model, in which the image u has been blurred by a noisy (stochastic) point spread function. We consider the problem of restoring the degraded image in a variational framework. Since the recovery of u from one single frame f
Miyoun Jung, Antonio Marquina, Luminita A. Vese   +2 more
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