Skip to main content
SpringerLink
Log in

Minimizing Aliasing Effects Using Faster Super Resolution Technique on Text Images

  • Chapter
  • First Online:
Transactions on Computational Science XXXI

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 10730))

Abstract

Image quality improvement is not bounded within the application of different types of filtering. Resolution improvement is also essential and it solely depends on the estimation of the unknown pixel value that involves a lot of computation. Here a resolution enhancement technique is proposed to reduce the aliasing effects from the text documented image with a reduced amount of computational time. The proposed hybrid method provides better resolution at most informative regions. Here, the unknown pixel value is estimated based on their local informative region. This technique finds the most informative areas, discontinuity at the edges and less informative areas separately. The foreground regions are segmented at the first phase. The unknown pixels values of the foreground regions are calculated in the second step. All-of-these separated images are combined together to construct the high-resolution image at the third phase. The proposed method is mainly verified on aliasing affected text documented images. A distinct advantage of the proposed method over other conventional approaches is that it requires lower computational time to construct a high-resolution image from a single low-resolution one.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tabedzki, M., Saeed, K., Szczepański, A.: A modified K3M thinning algorithm. Int. J. Appl. Math. Comput. Sci. 26(2), 439–450 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  2. Buczkowski, M., Saeed, K.: Fusion-based noisy image segmentation method. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds.) Advanced Computing and Systems for Security. AISC, vol. 396, pp. 21–35. Springer, New Delhi (2016). https://doi.org/10.1007/978-81-322-2653-6_2

    Chapter  Google Scholar 

  3. Fujisawa, H.: Forty years of research in character and document recognition—an industrial perspective. Pattern Recogn. 41(8), 2435–2446 (2008)

    Article  Google Scholar 

  4. Yuan, L., Sun, J., Quan, L., Shum, H.Y.: Image deblurring with blurred/noisy image pairs. ACM Trans. Graph. (TOG) 26, 1 (2007). ACM

    Article  Google Scholar 

  5. Jimenez, J., Echevarria, J.I., Sousa, T., Gutierrez, D.: SMAA: enhanced subpixel morphological antialiasing. Comput. Graph. Forum 31, 355–364 (2012). Wiley Online Library

    Article  Google Scholar 

  6. Papandreou, A., Gatos, B.: A novel skew detection technique based on vertical projections. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 384–388. IEEE (2011)

    Google Scholar 

  7. LatifoğLu, F.: A novel approach to speckle noise filtering based on artificial bee colony algorithm: an ultrasound image application. Comput. Methods Programs Biomed. 111(3), 561–569 (2013)

    Article  Google Scholar 

  8. Chen, J., Benesty, J., Huang, Y., Doclo, S.: New insights into the noise reduction Wiener filter. IEEE Trans. Audio Speech Lang. Process. 14(4), 1218–1234 (2006)

    Article  Google Scholar 

  9. Fan, K.C., Wang, Y.K., Lay, T.R.: Marginal noise removal of document images. Pattern Recogn. 35(11), 2593–2611 (2002)

    Article  MATH  Google Scholar 

  10. Nyquist, H.: Certain topics in telegraph transmission theory. Proc. IEEE 90(2), 280–305 (2002)

    Article  Google Scholar 

  11. Kaur, J., Kaur, M., Kaur, P., Kaur, M.: Comparative analysis of image denoising techniques. Int. J. Emerg. Technol. Adv. Eng. 2(6), 296–298 (2012)

    MATH  Google Scholar 

  12. Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Process. Mag. 20(3), 21–36 (2003)

    Article  Google Scholar 

  13. Nasrollahi, K., Moeslund, T.B.: Super-resolution: a comprehensive survey. Mach. Vis. Appl. 25(6), 1423–1468 (2014)

    Article  Google Scholar 

  14. Li, X., Lam, K.M., Qiu, G., Shen, L., Wang, S.: Example-based image super-resolution with class-specific predictors. J. Vis. Commun. Image Represent. 20(5), 312–322 (2009)

    Article  Google Scholar 

  15. Thouin, P.D., Chang, C.I.: A method for restoration of low-resolution document images. Int. J. Doc. Anal. Recogn. 2(4), 200–210 (2000)

    Article  Google Scholar 

  16. Park, J., Kwon, Y., Kim, J.H.: An example-based prior model for text image super-resolution. In: Proceedings of the Eighth International Conference on Document Analysis and Recognition, pp. 374–378. IEEE (2005)

    Google Scholar 

  17. Kim, H.Y.: Binary operator design by k-nearest neighbor learning with application to image resolution increasing. Int. J. Imaging Syst. Technol. 11(5), 331–339 (2000)

    Article  Google Scholar 

  18. Ho, T.C., Zeng, B.: Super-resolution image by curve fitting in the threshold decomposition domain. In: IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2008, pp. 332–335. IEEE (2008)

    Google Scholar 

  19. Shan, Q., Li, Z., Jia, J., Tang, C.K.: Fast image/video upsampling. ACM Trans. Graph. (TOG) 27(5), 153 (2008)

    Article  Google Scholar 

  20. Datta, S., Chaki, N., Choudhury, S.: Information density based image binarization for text document containing graphics. In: Saeed, K., Homenda, W. (eds.) CISIM 2016. LNCS, vol. 9842, pp. 105–115. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45378-1_10

    Chapter  Google Scholar 

  21. Gonzalez, R.C., Woods, R.E.: Image processing. In: Digital Image Processing, vol. 2 (2007)

    Google Scholar 

  22. Ando, S.: Consistent gradient operators. IEEE Trans. Pattern Anal. Mach. Intell. 22(3), 252–265 (2000)

    Article  Google Scholar 

  23. Ghosh, P., Bhattacharjee, D., Nasipuri, M.: Blood smear analyzer for white blood cell counting: a hybrid microscopic image analyzing technique. Appl. Soft Comput. 46, 629–638 (2016)

    Article  Google Scholar 

  24. Southern California: USC-SIPI image database. University of Southern California. http://sipi.usc.edu/database/

  25. von Ghega, C.R.: Ghega-dataset: a dataset for document understanding and classification. http://machinelearning.inginf.units.it/data-and-tools/ghega-dataset

  26. Jagalingam, P., Hegde, A.V.: A review of quality metrics for fused image. Aquat. Procedia 4, 133–142 (2015)

    Article  Google Scholar 

  27. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  28. Hanhart, P., Bernardo, M.V., Pereira, M., Pinheiro, A.M., Ebrahimi, T.: Benchmarking of objective quality metrics for HDR image quality assessment. EURASIP J. Image Video Process. 2015(1), 39 (2015)

    Article  Google Scholar 

  29. Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153–1160 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  30. Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10(10), 1521–1527 (2001)

    Article  Google Scholar 

  31. Zheng, Y., Kang, X., Li, S., He, Y., Sun, J.: Real-time document image super-resolution by fast matting. In: 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 232–236. IEEE (2014)

    Google Scholar 

Download references

Acknowledgement

I would like to acknowledge Visvesvaraya PhD Scheme for Electronics and IT. I am also thankful to Department of Computer Science and Engineering, University of Calcutta for infrastructural supports.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Calcutta, JD Block, Sector III, Saltlake, Kolkata, India

    Soma Datta & Nabendu Chaki

  2. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

Authors
  1. Soma Datta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nabendu Chaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khalid Saeed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soma Datta .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  2. Sardina Systems OÜ, Tallinn, Estonia

    C.J. Kenneth Tan

  3. University of Calcutta, Calcutta, India

    Nabendu Chaki

  4. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer-Verlag GmbH Germany

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Datta, S., Chaki, N., Saeed, K. (2018). Minimizing Aliasing Effects Using Faster Super Resolution Technique on Text Images. In: Gavrilova, M., Tan, C., Chaki, N., Saeed, K. (eds) Transactions on Computational Science XXXI. Lecture Notes in Computer Science(), vol 10730. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56499-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-56499-8_7

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-56498-1

  • Online ISBN: 978-3-662-56499-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation