1. Blanchet, G., Moisan, L.: An explicit sharpness index related to global phase coherence. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1065–1068. IEEE (2012)

   Google Scholar 

2. Blando, L.R., Kanai, J., Nartker, T.A.: Prediction of OCR accuracy using simple image features. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 319–322. IEEE (1995)

   Google Scholar 

3. Brenner, J.F., Dew, B.S., Horton, B., King, T., Neurath, P.W., Selles, W.D.: An automated microscope for cytologic research a preliminary evaluation. Journal of Histochemistry & Cytochemistry 24(1), 100–111 (1976)

   Article  Google Scholar 

4. Cao, G., Zhao, Y., Ni, R.: Edge-based blur metric for tamper detection. Journal of Information Hiding and Multimedia Signal Processing 1(1), 20–27 (2010)

   Google Scholar 

5. Chen, M.-J., Bovik, A.C.: No-reference image blur assessment using multiscale gradient. EURASIP Journal on Image and Video Processing 2011(1), 1–11 (2011)

   Article MATH  Google Scholar 

6. Ciancio, A., da Costa, A.L.N.T., da Silva, E.A.B., Said, A., Samadani, R., Obrador, P.: No-reference blur assessment of digital pictures based on multifeature classifiers. IEEE Transactions on Image Processing 20(1), 64–75 (2011)

   Article MathSciNet  Google Scholar 

7. Fabrizio, J., Marcotegui, B., Cord, M.: Text segmentation in natural scenes using toggle-mapping. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 2373–2376. IEEE (2009)

   Google Scholar 

8. Firestone, L., Cook, K., Culp, K., Talsania, N., Preston, K.: Comparison of autofocus methods for automated microscopy. Cytometry 12(3), 195–206 (1991)

   Article  Google Scholar 

9. Groen, F.C.A., Young, I.T., Ligthart, G.: A comparison of different focus functions for use in autofocus algorithms. Cytometry 6(2), 81–91 (1985)

   Article  Google Scholar 

10. Hassen, R., Wang, Z., Salama, M.: No-reference image sharpness assessment based on local phase coherence measurement. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 2434–2437. IEEE (2010)

    Google Scholar 

11. Jarvis, R.A.: Focus optimization criteria for computer image-processing. Microscope 24(2), 163–180 (1976)

    Google Scholar 

12. Kramer, H.P., Bruckner, J.B.: Iterations of a non-linear transformation for enhancement of digital images. Pattern Recognition 7(1), 53–58 (1975)

    Article MATH MathSciNet  Google Scholar 

13. Kumar, J., Ye, P., Doermann, D.: A dataset for quality assessment of camera captured document images. In: Iwamura, M., Shafait, F. (eds.) CBDAR 2013. LNCS, vol. 3857, pp. 113–125. Springer, Heidelberg (2014)

    Google Scholar 

14. Leclaire, A., Moisan, L., et al.: No-reference image quality assessment and blind deblurring with sharpness metrics exploiting fourier phase information (2014)

    Google Scholar 

15. Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T.: Perceptual blur and ringing metrics: application to jpeg2000. Signal Processing: Image Communication 19(2), 163–172 (2004)

    Google Scholar 

16. Nayar, S.K., Nakagawa, Y.: Shape from focus. IEEE Transactions on Pattern analysis and machine intelligence 16(8), 824–831 (1994)

    Article  Google Scholar 

17. Chern, N.N.K., Neow, P.A., Ang, V.M.H.: Practical issues in pixel-based autofocusing for machine vision. In: Proceedings of 2001 IEEE International Conference on Robotics and Automation, ICRA, vol. 3, pp. 2791–2796. IEEE (2001)

    Google Scholar 

18. Peng, X., Cao, H., Subramanian, K., Prasad, R., Natarajan, P.: Automated image quality assessment for camera-captured OCR. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 2621–2624. IEEE (2011)

    Google Scholar 

19. Pertuz, S., Puig, D., Garcia, M.A.: Analysis of focus measure operators for shape-from-focus. Pattern Recognition 46(5), 1415–1432 (2013)

    Article MATH  Google Scholar 

20. Rusiñol, M., Chazalon, J., Ogier, J.-M.: Combining focus measure operators to predict OCR accuracy in mobile-captured document images. In: 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 181–185. IEEE (2014)

    Google Scholar 

21. Schlag, J.F., Sanderson, A.C., Neuman, C.P., Wimberly, F.C.: Implementation of automatic focusing algorithms for a computer vision system with camera control. Carnegie-Mellon Univ., the Robotics Inst. (1983)

    Google Scholar 

22. Serra, J.: Toggle mappings. From pixels to features,61–72 (1988)

    Google Scholar 

23. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839–846. IEEE (1998)

    Google Scholar 

24. Wang, Z., Bovik, A.C.: Reduced-and no-reference image quality assessment. IEEE Signal Processing Magazine 28(6), 29–40 (2011)

    Article  Google Scholar 

25. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)

    Article  Google Scholar 

26. Ye, P., Doermann, D.: Learning features for predicting OCR accuracy. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3204–3207. IEEE (2012)

    Google Scholar 

Download references