Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Linear Gaussian blur evolution for detection of blurry images

Linear Gaussian blur evolution for detection of blurry images

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Published

Even though state-of-the-art digital cameras are equipped with auto-focusing and motion compensation functions, several other factors including limited contrast, inappropriate exposure time and improper device handling can still lead to unsatisfactory image quality such as blurriness. Indeed, blurry images make up a significant percentage of anyone's picture collections. Consequently, an efficient tool to detect blurry images and label or separate them for automatic deletion in order to preserve storage capacity and the quality of image collections is needed. A new technique for automatic detection and removal of blurry pictures is presented. Initially, a set of interest points and local image areas is extracted. These areas are then evolved in time according to the conventional linear scale space. The gradient of the evolution curve through scale is then used to produce a ‘blur graph’ representing the probability of a picture being blurred or not. Complexity is kept low by applying a Monte-Carlo like technique for the selection of representative image areas and interest points and by implicitly estimating the gradient of the scale-space curve evolution. An exhaustive evaluation of the proposed technique is conducted to validate its performance in terms of detection accuracy and efficiency.

Inspec keywords: Gaussian processes; motion compensation; object detection; feature extraction; image restoration

Other keywords: blurry image detection; image collections; automatic deletion; conventional linear scale space; motion compensation functions; Monte-Carlo like technique; blur graph; scale-space curve evolution; digital cameras; autofocusing; image extraction; linear Gaussian blur evolution

Subjects: Optical, image and video signal processing; Other topics in statistics; Other topics in statistics; Computer vision and image processing techniques

References

    1. 1)
      • Vedaldi, A.: `An open implementation of SIFT detector and descriptor', 070012, UCLA CSD Technical, 2006.
    2. 2)
      • Zhang, Q., Chen, Y., Zhang, Y., Xu, Y.: `SIFT implementation and optimization for multi-core systems', IEEE Int. Symp. on Parallel and Distributed Processing, 2008, p. 1–8.
    3. 3)
      • L. Firestone , K. Cook , K. Culp , N. Talsania , K. Preston . Comparison of autofocus methods for automated microscopy. Cytometry , 195 - 206
    4. 4)
      • P. Marziliano , F. Dufaux , S. Winkler , T. Ebrahimi . Perceptual blur and ringing metrics: application to JPEG 2000. Signal Process.: Image Commun. , 163 - 172
    5. 5)
      • Brown, M., Lowe, D.G.: `Invariant features from interest point groups', Proc. British Machine Vision Conf., 2002, p. 656–665.
    6. 6)
      • Marichal, X., Ma, W.Y., Zhang, H.J.: `Blur determination in the compressed domain using DCT information', Proc. IEEE Int. Conf. Image Processing, 1999, p. 386–390.
    7. 7)
      • S.J. Erasmus , K.C.A. Smith . An automatic focusing and astigmatism correction system for the SEM and CTEM. J. Microsc. , 185 - 189
    8. 8)
      • Witkin, A.P.: `Scale-space filtering', Proc. Int. Conf. Artificial Intelligence, 1983, p. 1019–1021.
    9. 9)
      • Tong, H., Mingjing, L., Hongjiang, Z., Changshui, Z.: `Blur detection for digital images using wavelet transform', IEEE Int. Conf. on Multimedia and Expo (ICME), 2004, p. 17–20.
    10. 10)
      • Liu, R., Li, Z., Jia, J.: `Image partial blur detection and classification', Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008, p. 1–8.
    11. 11)
      • Lim, S., Yen, J., Wu, P.: `Detection of out-of-focus digital photographs', HPL-2005-14, Technical, 2005.
    12. 12)
      • Caviedes, J., Gurbuz, S.: `No-reference sharpness metric based on local edge kurtosis', Proc. IEEE Int. Conf. on Image Processing, 2002, 3, p. 53–56.
    13. 13)
      • Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T.: `A no-reference perceptual blur metric', Proc. Int. Conf. Image Processing, 2002, 3, p. 57–60.
    14. 14)
      • Tsomko, E., Kim, H.J.: `Efficient method of detecting globally blurry or sharp images', Proc. Ninth Int. Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), May 2008, Klagenfurt, Austria, p. 171–174.
    15. 15)
      • C.F. Batten , D.M. Holburn , B.C. Breton , N.H.M. Caldwell . Sharpness search algorithms for automatic focusing in the scanning electron microscope. J. Scanning Microsc. , 2 , 112 - 113
    16. 16)
    17. 17)
      • E. Izquierdo , M. Ghanbari . Key components for an advanced segmentation system. IEEE Trans. Multimedia , 1 , 97 - 113
    18. 18)
      • Shaked, D., Tastl, I.: `Sharpness measure: Towards automatic image enhancement', HPL-2004-84R-2, Technical, 2005.
    19. 19)
      • Boult, B.E., Chiang, M.C.: `Local blur estimation and super-resolution', Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1997, p. 821–826.
    20. 20)
      • Tico, M., Trimeche, M., Vehvilainen, M.: `Motion blur identification based on differently exposed images', IEEE Int. Conf. Image Processing, 2006, p. 2021–2024.
    21. 21)
      • Hu, H., Haan, G.: `Low cost robust blur estimator', Proc. IEEE, Int. Conf. Image Processing, 2006, p. 617–620.
    22. 22)
      • Ong, E., Lin, W., Lu, Z.: `A no-reference quality metric for measuring image blur', Proc. Seventh Int. Symp. on Signal Processing and its Applications, 2003, 1, p. 469–472.
    23. 23)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2009.0001
Loading

Related content

content/journals/10.1049/iet-ipr.2009.0001
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address