Skip to main content
Springer Nature Link
Log in

Text-image super-resolution through anchored neighborhood regression with multiple class-specific dictionaries

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In the dictionary-based image super-resolution (SR) methods, the resolution of the input image is enhanced using a dictionary of low-resolution (LR) and high-resolution (HR) image patches. Typically, a single dictionary is learned from all the patches in the training set. Then, the input LR patch is super-resolved using its nearest LR patches and their corresponding HR patches in the dictionary. In this paper, we propose a text-image SR method using multiple class-specific dictionaries. Each dictionary is learned from the patches of images of a specific character in the training set. The input LR image is segmented into text lines and characters, and the characters are preliminarily classified. Likewise, overlapping patches are extracted from the input LR image. Then, each patch is super-resolved through the anchored neighborhood regression, using n class-specific dictionaries corresponding to the top-n classification results of the character containing the patch. The final HR image is generated by aggregating all the super-resolved patches. Our method achieves significant improvements in visual image quality and OCR accuracy, compared to the related dictionary-based SR methods. This confirms the effectiveness of applying the preliminary character classification results and multiple class-specific dictionaries in text-image SR.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Since the characters are at very low resolution, their classification results are not reliable. Therefore, the top-n predicted classes are utilized.

References

  1. Dai, D., Wang, Y., Chen, Y., Van Gool, L.: Is image super-resolution helpful for other vision tasks? In: IEEE Winter Conference on Applications of Computer Vision (WACV) (2016)

  2. Abedi, A., Kabir, E.: Stroke width-based directional total variation regularisation for document image super resolution. IET Image Process. 10, 158–166 (2016)

    Article  Google Scholar 

  3. Walha, R., Drira, F., Lebourgeois, F., Alimi, A.M., Garcia, C.: Resolution enhancement of textual images: a survey of single image-based methods. IET Image Process. 10, 325–337 (2016)

    Article  Google Scholar 

  4. Chen, X., Qi, C.: Document image super-resolution using structural similarity and Markov random field. IET Image Process. 8, 687–698 (2014)

    Article  Google Scholar 

  5. Peyrard, C., Baccouche, M., Mamalet, F., Garcia, C.: ICDAR2015 competition on text image super-resolution. In: 13th International Conference on Document Analysis and Recognition (ICDAR 2015), pp. 1201–1205 (2015)

  6. Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M.: Resolution enhancement of textual images via multiple coupleddictionaries and adaptive sparse representation selection. Int. J. Doc. Anal. Recognit. IJDAR 18, 87–107 (2015)

    Article  Google Scholar 

  7. Chang, H., Yeung, D.-Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2004. CVPR 2004, pp. I–I (2004)

  8. Jiang, J., Hu, R., Wang, Z., Han, Z.: Face super-resolution via multilayer locality-constrained iterative neighbor embedding and intermediate dictionary learning. IEEE Trans. Image Process. 23, 4220–4231 (2014)

    Article  MathSciNet  Google Scholar 

  9. Datsenko, D., Elad, M.: Example-based single document image super-resolution: a global MAP approach with outlier rejection. Multidimens. Syst. Signal Process. 18, 103–121 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  10. Zhang, K., Gao, X., Li, X., Tao, D.: Partially supervised neighbor embedding for example-based image super-resolution. IEEE J. Sel. Top. Signal Process. 5, 230–239 (2011)

  11. Timofte, R., De, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: IEEE International Conference on Computer Vision (ICCV), 2013, pp. 1920–1927 (2013)

  12. Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) Computer Vision – ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1–5, 2014, Revised Selected Papers, Part IV, ed Cham: Springer International Publishing, pp. 111–126 (2015)

  13. Timofte, R., De Smet, V., Van Gool, L.: Semantic super-resolution: when and where is it useful? Comput. Vis. Image Underst. 142, 1–12 (2016)

    Article  Google Scholar 

  14. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P. , Cohen, A., Gout, C., Lyche, T., Mazure, M.-L. et al. (Eds.) Curves and Surfaces: 7th International Conference, Avignon, France, June 24–30, 2010, Revised Selected Papers, pp. 711–730. Springer, Berlin (2012)

  15. Jiang, J., Ma, X., Cai, Z., Hu, R.: Sparse support regression for image super-resolution. Photonics J. IEEE 7, 1–11 (2015)

    Google Scholar 

  16. Jiang, J., Hu, R., Han, Z., Lu, T.: Efficient single image super-resolution via graph-constrained least squares regression. Multimed. Tools Appl. 72, 2573–2596 (2014)

    Article  Google Scholar 

  17. Sun, J., Zhu, J., Tappen, M.F.: Context-constrained hallucination for image super-resolution. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 231–238 (2010)

  18. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006)

    Article  Google Scholar 

  19. Zhu, Z., Guo, F., Yu, H., Chen, C.: Fast single image super-resolution via self-example learning and sparse representation. IEEE Trans. Multimed. 16, 2178–2190 (2014)

    Article  Google Scholar 

  20. Yeganli, F., Nazzal, M., Ozkaramanli, H.: Image super-resolution via sparse representation over multiple learned dictionaries based on edge sharpness. Signal Image Video Process. 10, 535–542 (2016)

    Article  Google Scholar 

  21. Lu, Y.: On the segmentation of touching characters. In: Proceedings of the Second International Conference on Document Analysis and Recognition, pp. 440–443 (1993)

  22. Liu, C.-L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognit. 36, 2271–2285 (2003)

    Article  MATH  Google Scholar 

  23. Nayef, N., Chazalon, J., Gomez-Kramer, P., Ogier, J.-M.: Efficient example-based super-resolution of single text images based on selective patch processing. In: 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 227–231 (2014)

  24. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)

    Article  Google Scholar 

  25. Smith, R.: An overview of the Tesseract OCR engine. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), Parana, pp. 629–633 (2007)

  26. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 295–307 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Ali Abedi & Ehsanollah Kabir

Authors
  1. Ali Abedi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ehsanollah Kabir
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ali Abedi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abedi, A., Kabir, E. Text-image super-resolution through anchored neighborhood regression with multiple class-specific dictionaries. SIViP 11, 275–282 (2017). https://doi.org/10.1007/s11760-016-0933-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0933-2

Keywords