Skip to main content
SpringerLink
Log in

Digital restoration of damaged color documents based on hyperspectral imaging and lattice associative memories

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

Abstract

This paper introduces an autonomous hybrid technique designed for the digital restoration of the missing parts and occluding artifacts in damaged historical or artistic color documents. For this purpose, a hyperspectral imaging device is used to acquire sets of images in the visible and near infrared ranges. Assuming the presence of linearly mixed pixels registered from the spectral images, our technique uses two lattice auto-associative memories to extract the set of pure pigments spectra. Fractional abundance maps indicating the distributions of each pigment along the image are obtained by spectral linear unmixing. These maps are then used to locate holes and cracks in the document under study. The restoration process is performed by the application of a modified morphological region filling algorithm, followed by a vectorial linear interpolation scheme to restore the original color appearance of the filled areas. For illustration purposes, our procedure has been applied successfully to the restoration of superimposed scripts and an art painting.

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

Similar content being viewed by others

Notes

  1. Although the abundance map for the white paper is not shown, it was also estimated by including its corresponding spectral signature as another column of S.

References

  1. Fisher, C., Kakoulli, I.: Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications. Rev. Conserv. 7, 3–16 (2006)

    Google Scholar 

  2. Barni, M., Bartolini, F., Cappellini, V.: Image processing for virtual restoration of artworks. IEEE Multimed. Mag. 7(2), 34–37 (2000)

    Article  Google Scholar 

  3. Baronti, S., Casini, A., Lotti, F., Porcinai, S.: Multispectral imaging system for the mapping of pigments in works of art by use of principal component analysis. Appl. Opt. 37, 1299–1309 (1998)

    Article  Google Scholar 

  4. Ware, G.A., Chabries, D.M., Christiansen, R.W., Brady, J.E., Martin, C.E.: Multispectral analysis of ancient Maya pigments: implications for the Naj Tunich Corpus. In: Proceeding of IEEE: IGARSS, Honolulu, HI, 24–28 July, pp. 2489–2491 (2000)

  5. Easton, R. L., Knox, K. T., Christens-Barry, W. A.: Multispectral imaging of the Archimedes palimpsest. In: Proceeding of IEEE: 32nd Applied Imagery Pattern Recognition Workshop, pp. 111–116 (2003)

  6. Rapantzikos, K., Balas, C.: Hyperspectral imaging: potential in non-destructive analysis of palimpsest. In: Proceeding of IEEE: ICIP, 11–14 Sept, vol. 2, pp. 618–621 (2005)

  7. Liang, H.: Advances in multispectral and hyperspectral imaging for archaeology and art conservation. Appl. Phys. A, 106, 309–323 (2012)

  8. Tonazzini, A., Savino, P., Salerno, E.: A non-stationary density model to separate overlapped texts in degraded documents. SIViP 9(1), 155–164 (2015)

    Article  Google Scholar 

  9. Kim, S.J., Deng, F., Brown, M.S.: Visual enhancement of old documents with hyperspectral imaging. Pattern Recognit. 44, 1461–1459 (2011)

    Article  Google Scholar 

  10. Hedjam, R., Cheriet, M.: Historical document image restoration using multispectral imaging system. Pattern Recognit. 46, 2297–2312 (2013)

    Article  Google Scholar 

  11. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Prentice Hall, New Jersey (2008). (Ch. 9)

    Google Scholar 

  12. Spagnolo, G.S., Somma, F.: Virtual restoration of cracks in digitized image of paintings. J. Phys. Conf. Ser. 249(1), 012059 (2010)

    Article  Google Scholar 

  13. Barni, M., Pelagotti, A., Piva, A.: Image processing for the analysis and conservation of paintings: opportunities and challenges. IEEE Signal Process. Mag. 22(5), 141–144 (2005)

    Article  Google Scholar 

  14. Ritter, G.X., Sussner, P., Díaz de León, J.L.: Morphological associative memories. IEEE Trans. Neural Netw. 9(2), 281–293 (1998)

    Article  Google Scholar 

  15. Graña, M., Chyzhyk, D.: Image understanding applications of lattice autoassociative memories. IEEE Trans. Neural Netw. Learn. Syst. 27(9), 1920–1932 (2016)

    Article  MathSciNet  Google Scholar 

  16. Ritter, G.X., Gader, P.: Fixed point of lattice transforms and lattice associative memories. In: Hawkes, P. (ed.) Advances in Imaging and Electron Physics, vol. 144, pp. 165–242. Elsevier, Amsterdam (2006)

    Google Scholar 

  17. Birkhoff, G.: Lattice Theory, 3rd edn. American Mathematical Society, Providence, RI (1967)

    MATH  Google Scholar 

  18. Kaburlasos, V.G., Ritter, G.X. (eds.): Computational Intelligence Based on Lattice Theory. Springer, Berlin (2007)

    MATH  Google Scholar 

  19. Keshava, N., Mustard, J.F.: Spectral unmixing. IEEE Signal Process. Mag. 19(1), 44–57 (2002)

    Article  Google Scholar 

  20. Lechuga-S, E., Valdiviezo-N, J.C., Urcid, G.: Multispectral image restoration of historical documents based on LAAMs and mathematical morphology. In: Proceeding of SPIE: Optics and Photonics for Information Processing VIII, San Diego, CA, 17 Aug 2014, p. 921604 (2014)

  21. Urcid, G., Ritter, G.X.: \(C\)-means clustering of lattice auto-associative memories for endmember approximation. In: Advances in Knowledge-Based and Intelligent Information Systems, vol. 243, pp. 2140–2149. IOS Press, Amsterdam (2012)

  22. Valdiviezo-N, J.C., Urcid, G.: Lattice algebra approach to multispectral analysis of ancient documents. Appl. Opt. 52(4), 674–682 (2013)

    Article  Google Scholar 

  23. Haralick, R.M., Sternberg, S.R., Zhuang, X.: Image analysis using mathematical morphology. IEEE Trans. Pattern Anal. Mach. Intell. 9(4), 532–550 (1987)

    Article  Google Scholar 

  24. Vincent, L.: Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2(2), 176–201 (1993)

    Article  Google Scholar 

  25. Soille, P.: Morphological Image Analysis: Principles and Applications, 2nd edn. Springer, Berlin (2003)

    MATH  Google Scholar 

  26. Ng, T., Lee, V., Gallagher, R., Coldman, A., McLean, D.: Dullrazor\(\textregistered \): a software approach to hair removal from images. Comput. Biol. Med. 27(6), 533–543 (1997)

    Article  Google Scholar 

  27. H.P.: Headwall Photonics. http://headwallphotonics.com

  28. Jacobson, N.P., Gupta, M.R.: Design goals and solutions for display of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 43(11), 2684–2692 (2005)

  29. Hedjam, R., Cheriet, M.: Ground-truth estimation in multispectral representation space: application to degraded document image binarization. In: Proceeding of IEEE: International Conference on Document Analysis and Recognition, Washington, DC, 25–28 Aug 2013, pp. 190–194 (2013)

  30. L.M.C.T.: Synchromedia: laboratory for multimedia communication in telepresence. http://synchromedia.ca/databases/msi-histodoc?page=1

Download references

Acknowledgements

Edwin Lechuga thanks the National Council of Science and Technology (CONACyT) for scholarship No. 556848. Juan C. Valdiviezo and Gonzalo Urcid are grateful with the National Research System (SNI-CONACyT) in Mexico city for partial support through Grant Nos. 57564 and 22036, respectively.

Author information

Authors and Affiliations

  1. Cátedras CONACYT-Centro de Investigación en Geografía y Geomática, Contoy 137, Tlalpan, 14240, Ciudad de México, México

    Juan C. Valdiviezo-N

  2. Coordinación de Óptica, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, 72000, Tonantzintla, México

    Gonzalo Urcid

  3. División de Ingenierías, Universidad Politécnica de Tulancingo, Ingenierías 100, 43629, Tulancingo, México

    Edwin Lechuga

Authors
  1. Juan C. Valdiviezo-N
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gonzalo Urcid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edwin Lechuga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan C. Valdiviezo-N.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Valdiviezo-N, J.C., Urcid, G. & Lechuga, E. Digital restoration of damaged color documents based on hyperspectral imaging and lattice associative memories. SIViP 11, 937–944 (2017). https://doi.org/10.1007/s11760-016-1042-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-1042-y

Keywords

Search

Navigation