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Restoration of the Blurred Image Based on Continuous Blur Kernel

2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2016
It's very common to see many regions of the blur in the pictures because of the relative movement of the subject and the shooting equipment, which causes much difficulty for the subsequent processing such as information extraction. This paper proposes a new method to solve the problem by using the continuous motion kernel.
Yuanzhi Gong, Yule Yuan, Wenbin Zou, Yong Zhao, Song Tang, Yuanyuan Qin   +5 more
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Partial Deconvolution With Inaccurate Blur Kernel

IEEE Transactions on Image Processing, 2018
Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to
Dongwei Ren, Xu Jun
exaly   +4 more sources

Unsupervised Blur Kernel Learning for Pansharpening

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
Deep learning (DL) for pansharpening has recently attracted considerable attentions. To construct training data, DL based pansharpening approaches often downsample the original multispectral image (MSI) and panchromatic image (PAN) with fixed blur kernel, which can be different from the real point spread functions (PSF) of the satellites.
Renwei Dian, Shutao Li, Anjing Guo
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   +3 more sources

Robust Image Deblurring With an Inaccurate Blur Kernel

IEEE Transactions on Image Processing, 2012
Most existing nonblind image deblurring methods assume that the blur kernel is free of error. However, it is often unavoidable in practice that the input blur kernel is erroneous to some extent. Sometimes, the error could be severe, e.g., for images degraded by nonuniform motion blurring. When an inaccurate blur kernel is used as the input, significant
Hui Ji
exaly   +4 more sources

Exposing Blur Kernel from Retouch Image

2013 International Conference on Computer-Aided Design and Computer Graphics, 2013
The blurring in image comes either from the acquisition noise, or from image editing operation. The produced adverse noise during acquisition need to be eliminated, and the blurring generated by editing should be known in digital forensics, so the blur kernel recovery is significant in community of image processing and computer graphics.
Zhenlong Du, Yanwen Guo
exaly   +2 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, Pan Chunhong
exaly   +3 more sources

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
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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 ...
Jue Wang
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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   +3 more sources