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Pre-processing Techniques for Detection of Blurred Images

  • Leena Mary FrancisEmail author
  • N. Sreenath
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 28)

Abstract

Blur detection and estimation have progressively became an imminent arena of computer vision. Along with heightening usage of mobiles and photographs, detecting the blur is purposed over to enhance or to remove the images. PrE-processing Techniques for DEtection of Blurred Images(PET-DEBI) was framed to detect the blurred and undistorted images. The frailty of Laplacian has been overcome by Gaussian filter to remove the noise of the image; then, the variance of Laplacian is calculated over the images. Through analysing the variance of the images, appropriate threshold is circumscribed and further used as limitation to define blurred and unblurred images. PET-DEBI was implemented and experimented yielding encouraging results with accuracy of 87.57%, precision of 88.88%, recall of 86.96% and F-measure of 87.91%.

Keywords

Blur detection Blur estimation Gaussian Filter Laplacian function Threshold fixing 

Notes

Acknowledgements

The research is funded by University Grants Commission as part of their programme called as Maulana Azad National Fellowship.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPondicherry Engineering CollegePondicherryIndia

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