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

access icon free Local multiscale blur estimation based on toggle mapping for sharp region extraction

© The Institution of Engineering and Technology
Received 14/02/2017, Accepted 15/06/2018, Revised 20/03/2018, Published 27/07/2018

In this study, a multiscale local blur estimation is proposed based on the existing local focus measure that combines gradient and toggle mapping. This method evaluates the quality of images regardless of their content (not in an autofocus context) and can predict Optical Character Recognition accuracy based on local blur. The resulting approach outperforms state of the art blur detection methods. Quantitative results are given on DIQA database. Moreover, the authors demonstrate its usefulness for extracting a region of interest from partially blurry images. Results are shown on images acquired by a project devoted to smartphone based text extraction for visually impaired people. In this study, sharp region extraction is essential since it allows warning the users when their picture is unusable. Moreover, it saves computing time.

Inspec keywords: image restoration; gradient methods; optical character recognition; edge detection

Other keywords: image quality evaluation; sharp region of interest extraction; toggle mapping; blur detection methods; optical character recognition; gradient mapping; visually impaired people; smartphone based text extraction; local focus measure; local multiscale blur estimation; DIQA database

Subjects: Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Image recognition

References

    1. 1)
      • 5. Groen, F.C.A., Young, I.T., Ligthart, G.: ‘A comparison of different focus functions for use in autofocus algorithms’, Cytometry, 1985, 6, (2), pp. 81–91.
    2. 2)
      • 27. Ye, P., Doermann, D.: ‘Learning features for predicting OCR accuracy’. 2012 21st Int. Conf. on Pattern Recognition (ICPR), 2012, pp. 3204–3207.
    3. 3)
      • 24. Pertuz, S., Puig, D., Garcia, M.A.: ‘Analysis of focus measure operators for shape-from-focus’, Pattern Recognit., 2013, 46, (5), pp. 1415–1432.
    4. 4)
      • 6. Santos, A., Ortiz de Solorzano, C., Vaquero, J.J., et al: ‘Evaluation of autofocus functions in molecular cytogenetic analysis’, J. Microsc., 1997, 188, (3), pp. 264–272.
    5. 5)
      • 19. Xie, H., Rong, W., Sun, L.: ‘Wavelet-based focus measure and 3-d surface reconstruction method for microscopy images’. 2006 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2006, pp. 229–234.
    6. 6)
      • 14. Cao, G., Zhao, Y., Ni, R.: ‘Edge-based blur metric for tamper detection’, J. Inf. Hiding Multimedia Signal Process., 2010, 1, (1), pp. 20–27.
    7. 7)
      • 1. Ponomarenko, N., Lukin, V., Zelensky, A., et al: ‘Tid2008-a database for evaluation of full-reference visual quality assessment metrics’, Adv. Mod. Radioelectron., 2009, 10, (4), pp. 30–45.
    8. 8)
      • 34. Tomasi, C., Manduchi, R.: ‘Bilateral filtering for gray and color images’. Sixth Int. Conf. on Computer Vision, 1998, 1998, pp. 839–846.
    9. 9)
      • 29. Kieu, V.-C., Cloppet, F., Vincent, N.: ‘OCR accuracy prediction method based on blur estimation’. 2016 12th IAPR Workshop on Document Analysis Systems (DAS), 2016, pp. 317–322.
    10. 10)
      • 31. Kramer, H.P., Bruckner, J.B.: ‘Iterations of a non-linear transformation for enhancement of digital images’, Pattern Recognit., 1975, 7, (1), pp. 53–58.
    11. 11)
      • 33. Fabrizio, J., Marcotegui, B., Cord, M.: ‘Text segmentation in natural scenes using toggle-mapping’. 2009 16th IEEE Int. Conf. on Image Processing (ICIP), 2009, pp. 2373–2376.
    12. 12)
      • 21. Wang, Z., Bovik, A.C.: ‘Reduced-and no-reference image quality assessment’, IEEE Signal Process. Mag., 2011, 28, (6), pp. 29–40.
    13. 13)
      • 3. Ma, L., Li, S., Zhang, F., et al: ‘Reduced-reference image quality assessment using reorganized DCT-based image representation’, IEEE Trans. Multimed., 2011, 13, (4), pp. 824–829.
    14. 14)
      • 25. Peng, X., Cao, H., Subramanian, K., et al: ‘Automated image quality assessment for camera-captured OCR’. 2011 18th IEEE Int. Conf. on Image Processing (ICIP), 2011, pp. 2621–2624.
    15. 15)
      • 28. Kumar, J., Ye, P., Doermann, D.: ‘A dataset for quality assessment of camera captured document images’. Camera-Based Document Analysis and Recognition, 2014, pp. 113–125.
    16. 16)
      • 23. Blanchet, G., Moisan, L.: ‘An explicit sharpness index related to global phase coherence’. 2012 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 1065–1068.
    17. 17)
      • 7. Yeo, T.T.E., Ong, S.H., Sinniah, R., et al: ‘Autofocusing for tissue microscopy’, Image Vis. Comput., 1993, 11, (10), pp. 629–639.
    18. 18)
      • 32. Serra, J.: ‘Toggle mappings’. From Pixels to Features, 1988, pp. 61–72.
    19. 19)
      • 4. Wang, Z., Simoncelli, E.P.: ‘Reduced-reference image quality assessment using a wavelet-domain natural image statistic model’. Electronic Imaging 2005, 2005, pp. 149–159.
    20. 20)
      • 2. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600–612.
    21. 21)
      • 8. Chen, M.-J., Bovik, A.C.: ‘No-reference image blur assessment using multiscale gradient’, EURASIP J. Image and Video Process., 2011, 2011, (1), pp. 1–11.
    22. 22)
      • 9. Nayar, S.K., Nakagawa, Y.: ‘Shape from focus’, IEEE Trans. Pattern Anal. Mach. Intell., 1994, 16, (8), pp. 824–831.
    23. 23)
      • 18. Yang, G., Nelson, B.J.: ‘Wavelet-based autofocusing and unsupervised segmentation of microscopic images’. Proc. 2003 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2003 (IROS 2003), 2003, vol. 3, pp. 2143–2148.
    24. 24)
      • 26. Blando, L.R., Kanai, J., Nartker, T.A.: ‘Prediction of OCR accuracy using simple image features’. Proc. of the Third Int. Conf. on Document Analysis and Recognition, 1995, 1995, vol. 1, pp. 319–322.
    25. 25)
      • 30. Chabardès, T., Marcotegui, B.: ‘Local blur estimation based on toggle mapping’. 12th Int. Symp. Mathematical Morphology and Its Applications to Signal and Image Processing, ISMM 2015, Reykjavik, Iceland, 27–29 May 2015, pp. 146–156.
    26. 26)
      • 17. Saad, M.A., Bovik, A.C., Charrier, C.: ‘Blind image quality assessment: a natural scene statistics approach in the DCT domain’, IEEE Trans. Image Process., 2012, 21, (8), pp. 3339–3352.
    27. 27)
      • 36. Faessel, M., Bilodeau, M.: ‘SMIL simple morphological image library’, Séminaire Performance et Généricité, LRDE, 2014.
    28. 28)
      • 22. Blanchet, G., Moisan, L., Rougé, B.: ‘Measuring the global phase coherence of an image’. 15th IEEE Int. Conf. on Image Processing, 2008, ICIP 2008, 2008, pp. 1176–1179.
    29. 29)
      • 13. Marziliano, P., Dufaux, F., Winkler, S., et al: ‘Perceptual blur and ringing metrics: application to jpeg2000’, Signal Process., Image Commun., 2004, 19, (2), pp. 163–172.
    30. 30)
      • 16. Marichal, X., Ma, W.-Y., Zhang, H.J.: ‘Blur determination in the compressed domain using DCT information’. Proc. 1999 Int. Conf. on Image Processing, 1999, ICIP 99, 1999, vol. 2, pp. 386–390.
    31. 31)
      • 11. Schlag, J.F., Sanderson, A.C., Neuman, C.P., et al: ‘Implementation of automatic focusing algorithms for a computer vision system with camera control’, Carnegie-Mellon Univ., the Robotics Inst., 1983.
    32. 32)
      • 10. Jarvis, R.A.: ‘Focus optimization criteria for computer image-processing’, Microscope, 1976, 24, (2), pp. 163–180.
    33. 33)
      • 15. Firestone, L., Cook, K., Culp, K., et al: ‘Comparison of autofocus methods for automated microscopy’, Cytometry, 1991, 12, (3), pp. 195–206.
    34. 34)
      • 35. Rusiñol, M., Chazalon, J., Ogier, J.-M.: ‘Combining focus measure operators to predict OCR accuracy in mobile-captured document images’. 2014 11th IAPR Int. Workshop on Document Analysis Systems (DAS), 2014, pp. 181–185.
    35. 35)
      • 20. Ciancio, A., Targino da Costa, A.L.N., da Silva, E.A.B., et al: ‘No-reference blur assessment of digital pictures based on multifeature classifiers’, IEEE Trans. Image Process., 2011, 20, (1), pp. 64–75.
    36. 36)
      • 12. Chern, N.N.K., Neow, P.A., Ang, V.M.H.: ‘Practical issues in pixel-based autofocusing for machine vision’. IEEE Int. Conf. on Robotics and Automation, 2001. Proc. 2001 ICRA, 2001, vol. 3, pp. 2791–2796.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2017.0095
Loading

Related content

content/journals/10.1049/iet-ipr.2017.0095
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address