Results 241 to 250 of about 2,159,265 (282)

Variational Dirichlet Blur Kernel Estimation

IEEE Transactions on Image Processing, 2015
Blind image deconvolution involves two key objectives: 1) latent image and 2) blur estimation. For latent image estimation, we propose a fast deconvolution algorithm, which uses an image prior of nondimensional Gaussianity measure to enforce sparsity and an undetermined boundary condition methodology to reduce boundary artifacts. For blur estimation, a
Xu Zhou, Javier Mateos, Aggelos K Katsaggelos   +2 more
exaly   +3 more sources

Some of the next articles are maybe not open access.

Related searches:

Blur-Kernel Bound Estimation From Pyramid Statistics

IEEE Transactions on Circuits and Systems for Video Technology, 2016
This letter presents an approach for automatically estimating the spatial bound of the blur kernel in a motion-blurred image based on the statistics of multilevel image gradients. We observe that blur has a significant impact on the histogram of oriented gradients (HOGs) at higher levels of an image pyramid, but has much less of an impact at coarser ...
Chunhong Pan, Jue Wang
exaly   +2 more sources

Joint blur kernel estimation and CNN for blind image restoration

Neurocomputing, 2020
Abstract Convolutional neural networks (CNN) have shown its excellent performance in computer vision fields. Recently, they are successfully applied to image restoration. This paper proposes a joint blur kernel estimation and CNN method for blind image restoration. The blur kernel estimation is based on both blur support parameter estimation and blur
Youshen Xia, Liqing Huang
exaly   +3 more sources

Automatic blur-kernel-size estimation for motion deblurring

Visual Computer, 2014
Existing image deblurring approaches often take the blur-kernel-size as an important manual parameter. When set improperly, this parameter can lead to significant errors in the estimated blur kernels. However, manually specifying a proper kernel size for an input image is usually a tedious trial-and-error process.
Sunghyun Cho, Jue Wang
exaly   +3 more sources

Motion Blur Kernel Estimation via Deep Learning

IEEE Transactions on Image Processing, 2018
The success of the state-of-the-art deblurring methods mainly depends on the restoration of sharp edges in a coarse-to-fine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images.
Xiangyu Xu, Jinshan Pan, Ming-Hsuan Yang
exaly   +4 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

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