Linear Gaussian blur evolution for detection of blurry images

Linear Gaussian blur evolution for detection of blurry images

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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.


    1. 1)
      • Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T.: `A no-reference perceptual blur metric', Proc. Int. Conf. Image Processing, 2002, 3, p. 57–60.
    2. 2)
      • P. Marziliano , F. Dufaux , S. Winkler , T. Ebrahimi . Perceptual blur and ringing metrics: application to JPEG 2000. Signal Process.: Image Commun. , 163 - 172
    3. 3)
      • 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.
    4. 4)
      • Boult, B.E., Chiang, M.C.: `Local blur estimation and super-resolution', Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1997, p. 821–826.
    5. 5)
      • Shaked, D., Tastl, I.: `Sharpness measure: Towards automatic image enhancement', HPL-2004-84R-2, Technical, 2005.
    6. 6)
      • Hu, H., Haan, G.: `Low cost robust blur estimator', Proc. IEEE, Int. Conf. Image Processing, 2006, p. 617–620.
    7. 7)
      • L. Firestone , K. Cook , K. Culp , N. Talsania , K. Preston . Comparison of autofocus methods for automated microscopy. Cytometry , 195 - 206
    8. 8)
      • 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.
    9. 9)
      • 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.
    10. 10)
      • Lim, S., Yen, J., Wu, P.: `Detection of out-of-focus digital photographs', HPL-2005-14, Technical, 2005.
    11. 11)
      • Liu, R., Li, Z., Jia, J.: `Image partial blur detection and classification', Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008, p. 1–8.
    12. 12)
      • 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.
    13. 13)
      • 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
    14. 14)
      • Vedaldi, A.: `An open implementation of SIFT detector and descriptor', 070012, UCLA CSD Technical, 2006.
    15. 15)
      • 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.
    16. 16)
      • Tico, M., Trimeche, M., Vehvilainen, M.: `Motion blur identification based on differently exposed images', IEEE Int. Conf. Image Processing, 2006, p. 2021–2024.
    17. 17)
      • 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.
    18. 18)
    19. 19)
      • Witkin, A.P.: `Scale-space filtering', Proc. Int. Conf. Artificial Intelligence, 1983, p. 1019–1021.
    20. 20)
      • E. Izquierdo , M. Ghanbari . Key components for an advanced segmentation system. IEEE Trans. Multimedia , 1 , 97 - 113
    21. 21)
    22. 22)
      • Brown, M., Lowe, D.G.: `Invariant features from interest point groups', Proc. British Machine Vision Conf., 2002, p. 656–665.
    23. 23)
      • S.J. Erasmus , K.C.A. Smith . An automatic focusing and astigmatism correction system for the SEM and CTEM. J. Microsc. , 185 - 189

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