Skip to main content
SpringerLink
Log in

A Chinese character structure preserved denoising method for Chinese tablet calligraphy document images based on KSVD dictionary learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

As an art form, Chinese ancient calligraphy tablet works occupy an important position in the heritage of Chinese culture. However, because of natural or man-made decay, there appear lots of noises in these ancient tablet works images, which have an important effect on the quality of the tablet images. To address this problem, a character structure preserved denoising method based on KSVD dictionary learning was proposed in this paper. This new proposed method consists of two major operations: dividing-frequency denoising and ant-like noises removal in binary image. At the stage of dividing-frequency denosing, the Butterworth low pass filter was employed to filter and extract low frequency part of the images firstly. Then, KSVD dictionary learning algorithm was used for smoothing the high frequency image and extracting image edges and the extracted edge images was then fused with the denoised low frequency part of the images. At the stage of ant-like noises removal, the fused image is converted into a binary one firstly. Then, the connected region method is employed to remove isolated ant-like noise; ergodic method is used to fill holes of strokes. Finally strokes thorn was eliminated by using the median filter. Experimental results demonstrate that the proposed method can effectively remove most image noise (including various block noise, linear noise and ant-like noise) and preserve characters better than existing methods.

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

Access this article

Log in via an institution

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Aharon M, Elad M, Bruckstein A (2006a) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322

  2. Aharon M, Elad M, Bruckstein A (2006b) On the uniqueness of overcomplete dictionaries, and a practical way to retrieve them. Linear Algebra Appl 48–67

  3. Aharon M, Elad M, Bruckstein A (2006c) The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322

  4. Buades A, Coll B, Morel JM (2008) Nonlocal image and movie denoising. Int J Comput Vis 76(2):123–139

    Article  Google Scholar 

  5. Butterworth S (1930) On the theory of filter amplifiers. Exp Wirel Wireless Eng 7:536–541

    Google Scholar 

  6. Chen J, Kang X, Liu Y et al (2015) Median filtering forensics based on convolutional neural networks. Signal Process Lett IEEE 22(11):1849–1853

    Article  Google Scholar 

  7. Dabov K, Foi A, Egiazarian K (2006) Image denoising with block-matching and 3D filtering. Electronic Imaging 354–365

  8. Dabov K, Foi A, Katkovnik V et al (2007a) Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 16(8):2080–2095

    Article  MathSciNet  Google Scholar 

  9. Dabov K, Foi A, Egiazarian K (2007b) Video denoising by sparse 3D transform-domain collaborative filtering. Signal Processing Conference. European. IEEE 2007:2080–2095

  10. Donoho DL, Elad M, Temlyakov VN (2006) Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans Inf Theory 52(1):6–18

    Article  MathSciNet  MATH  Google Scholar 

  11. Elad M, Aharon M (2006a) Image denoising via sparse and redundant representation over learned dictionaries. IEEE Trans Image Process 15(12):3376–3745

    Article  MathSciNet  Google Scholar 

  12. Elad M, Aharon M (2006b) Image denoising via learned dictionaries and sparse representation 1(1):895–900

  13. Golub GH, Van-Loan CF (1996) Matrix computations, 3rd ed. John Hopkins Univ. Press, Baltimore

    MATH  Google Scholar 

  14. Guemri K, Drira F (2014) Adaptative shock filter for image characters enhancement and denoising. Soft Computing and Pattern Recognition. IEEE 839–43

  15. Koga T, Uchino E, Suetake N, et al. (2008) Edge-preserved smoothing method with special reference to intravascular ultrasound image using anisotropic diffusion filter controlled by weighted separability measure. Conference on Circuits, Systems, Electronics, Control and Signal Processing. 87–92

  16. Li H, Suen C Y (2015) A novel non-local means image denoising method based on grey theory pattern recognition

  17. Lin HM, Willson AN (1988) Median filters with adaptive length. IEEE Trans Circ Syst 35(6):675–690

    Article  MathSciNet  Google Scholar 

  18. Mallat S, Zhang Z (1993) Matching pursuit in a time-frequency dictionary. IEEE Trans Signal Process 41(12):3397–3415

    Article  MATH  Google Scholar 

  19. Nguyen HT, Linh-Trung N (2010) The laplacian pyramid with rational scaling factors and application on image denoising. International Conference on Information Sciences Signal Processing and Their Applications. IEEE 468–471

  20. Pati YC, Rezaiifar R, Krishnaprasad PS (1995) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. Conference on Signals. IEEE 1–3

  21. Shi Z, Xu B, Zheng X et al (2016) An integrated method for ancient Chinese tablet images de-noising based on assemble of multiple image smoothing filters. Multimed Tools Appl 75(19):12245–12261

  22. Tropp JA (2004) Greed is good: algorithmic results for sparse approximation. IEEE Trans Inf Theory 50(10):2231–2242

    Article  MathSciNet  MATH  Google Scholar 

  23. Tukey JW (1971) Exploratory data analysis (preliminary ed). Addision-Wesley, Reading

    Google Scholar 

  24. Wang S Z, Lee H J (2001) Dual-binarization and anisotropic diffusion of chinese characters in calligraphy documents. International Conference on Document Analysis and Recognition. Proceedings 271–275

  25. Zhang J, Yu J-h et al (2006) Denoising of Chinese calligraphy tablet images based on run-length statistics and structure characteristic of character strokes. J Zhejiang Univ Sci A 7(7):1178–1186

    Article  MATH  Google Scholar 

  26. Zheng X, Miao Q, Shi Z et al (2015) A new artistic information extraction method with multi channels and guided filters for calligraphy works. Multimed Tools Appl 75(14):8719–8744

Download references

Acknowledgements

This work was supported in part by a grant from the National Natural Science Foundation of China (No. 61202198, No.61401355 ), a grant from the China Scholarship Council (No.201608610048), the Key Laboratory Foundation of Shaanxi Education Department, China (No.14JS072), and the Nature Science Foundation of Science Department of PeiLin count at Xi’an(GX1619). The authors gratefully acknowledge the helpful comments and suggestions of the reviewers.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, Shaanxi, 710048, China

    Zhenghao Shi, Binxin Xu & Minghua Zhao

  2. Department of Culture Heritage and Museology, Zhejiang University, Hangzhou, Zhejiang, 310028, China

    Xia Zheng

Authors
  1. Zhenghao Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Binxin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xia Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Minghua Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhenghao Shi or Xia Zheng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, Z., Xu, B., Zheng, X. et al. A Chinese character structure preserved denoising method for Chinese tablet calligraphy document images based on KSVD dictionary learning. Multimed Tools Appl 76, 14921–14936 (2017). https://doi.org/10.1007/s11042-016-4284-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4284-3

Keywords

Search

Navigation