NDLI: Spatially varying blur estimation from a single image

Content Provider	IET Digital Library
Author	Shekheby, Shereen El Kader, Rehab F. Abdel Zaki, Fayez W.
Abstract	Detection of single image spatially varying blur is a challenging task. Here, the authors propose a powerful spatially varying blur detection method. Blur is detected from a single image without requiring any prior knowledge about the parametric blur kernel, the camera settings, or information about the input image. The proposed approach detects both motion and defocus blurred regions in partially blurred images. First, blur-type classification is performed using Hough transform and the structured learning edge-detection method. Second, the initial blur kernel is estimated with a kernel direction that maximises the likelihood of a local window being blurred by incorporating either vertical or positive diagonal kernels. Blur detection is implemented using a kernel-specific feature. Third, initial blur image regions are refined with the support of the reduced edges image segmentation (CCP) method. Finally, neighbouring information is utilised to refine detected blur results. Comparative results reveal accuracy improvements in the image blur detection results at a reasonable execution time.
Starting Page	746
Ending Page	753
Page Count	8
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 5, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5663
Journal	IET Image Processing
Publisher Date	2019-01-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Blur Estimation Blur-type Classification Computer Vision And Image Processing Technique Detected Blur Result Edge Detection Hough Transforms Image Blur Detection Result Image Motion Analysis Image Recognition Image Restoration Image Segmentation Incorporating Initial Blur Image Region Initial Blur Kernel Input Image Kernel Direction Kernel-specific Feature Object Detection Optical, Image And Video Signal Processing Parametric Blur Kernel Partially Blurred Image Positive Diagonal Kernels Powerful Spatially Varying Blur Detection Method Reduced Edge Image Segmentation Method Single Image Statistics Structured Learning Edge-detection Method
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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