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)


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%.


Blur detection Blur estimation Gaussian Filter Laplacian function Threshold fixing 



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


  1. 1.
    Huang R, Feng W, Fan M, Wan L, Sun J (2018) Multiscale blur detection by learning discriminative deep features. Neurocomputing 285:154–166CrossRefGoogle Scholar
  2. 2.
    Kieu VC, Cloppet F, Vincent N (2017) Adaptive fuzzy model for blur estimation on document images. Pattern Recognition Letters 86:42–48CrossRefGoogle Scholar
  3. 3.
    Pendyala S, Ramesha P, Bns AV, Arora D (2015) Blur detection and fast blind image deblurring. In: India Conference (INDICON), 2015 Annual IEEE. pp 1–4Google Scholar
  4. 4.
    Rooms F, Pizurica A, Philips W (2002) Estimating image blur in the wavelet domain. In: IEEE International Conference on Acoustics Speech and Signal Processing. IEEE; 1999 vol. 4, pp 4190–4190Google Scholar
  5. 5.
    Sieberth T, Wackrow R, Chandler JH (2016) Automatic detection of blurred images in uav image sets. ISPRS Journal of Photogrammetry and Remote Sensing 122:1–16CrossRefGoogle Scholar
  6. 6.
    Soleimani S, Rooms F, Philips W (2013) Efficient blur estimation using multi-scale quadrature filters. Signal processing 93(7):1988–2002CrossRefGoogle Scholar
  7. 7.
    Su B, Lu S, Tan CL (2011) Blurred image region detection and classification. In: Proceedings of the 19th ACM international conference on Multimedia. ACM pp 1397–1400.Google Scholar
  8. 8.
    Tran GS, Nghiem TP, Doan NQ, Drogoul A, Mai LC (2016) Fast parallel blur detection of digital images. In: IEEE RIVF international conference on computing & communication technologies, research, innovation, and vision for the future (RIVF), IEEE (2016) pp 147–152.Google Scholar
  9. 9.
    Williams BM, Al-Bander B, Pratt H, Lawman S, Zhao Y, Zheng Y, Shen Y (2017) Fast blur detectionand parametric deconvolution of retinal fundus images. In: Fetal, infant and ophthalmic medical image analysis, Springer, pp 194–201Google Scholar
  10. 10.
    Wu S, Lin W, Xie S, Lu Z, Ong EP, Yao S (2009) Blind blur assessment for vision-based applications. Journal of Visual Communication and Image Representation 20(4):231–241CrossRefGoogle Scholar
  11. 11.
    Yang D, Qin S (2015) Restoration of degraded image with partial blurred regions based onblur detection and classification. In: 2015 IEEE international conference on mechatronics and automation (ICMA) IEEE, pp 2414–2419Google Scholar

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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