[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Blind Source Separation of Color Noisy Blurred Images

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Abstract

Blind Source Separation (BSS) is a relevant problem in the signal processing research area, with a broad application sphere. In this work, we consider a BSS problem with recto–verso images subject to show–through and bleed–through effects; in particular, we suppose that the observed image is obtained using a two-step linear process: the recto and verso images are separately blurred using a blur operator with known coefficients and then mixed. Our algorithm, Blind Estimation Technique Imposing Smoothness and Non–Overlapping (BETIS–NO), built on top of previous work, computes the entries of the mixture matrix, which regulates the intensity of recto–verso mixing for the document reconstruction; the introduction of second–order discontinuities and additional constraints on source images, i.e., minimum entropy, non–Gaussianity, and correct overlapping, leads to improved quality of the reconstruction.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 79.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 99.99
Price includes VAT (United Kingdom)
  • 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. Barros, A.K.: The independence assumption: dependent component analysis. In: Girolami, M. (eds.) Advances in Independent Component Analysis. Perspectives in Neural Computing. Springer, London (2000). https://doi.org/10.1007/978-1-4471-0443-8_4

  2. Benlin, X., Fangfang, L., Xingliang, M., Huazhong, J.: Study on independent component analysis application in classification and change detection of multispectral images. In: The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences, vol. 37, pp. 871–876 (2008)

    Google Scholar 

  3. Boccuto, A., Gerace, I.: Image reconstruction with a non-parallelism constraint. In: Proceedings of the International Workshop on Computational Intelligence for Multimedia Understanding, Reggio Calabria, Italy, 27–28 October 2016, pp. 1–5. IEEE Conference Publications (2016)

    Google Scholar 

  4. Boccuto, A., Gerace, I., Giorgetti, V.: A blind source separation technique for document restoration. SIAM J. Imaging Sci. 12(2), 1135–1162 (2019)

    Article  MathSciNet  Google Scholar 

  5. Boccuto, A., Gerace, I., Martinelli, F.: Half-quadratic image restoration with a non-parallelism constraint. J. Math. Imaging Vis. 59(2), 270–295 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  6. Boccuto, A., Gerace, I., Pucci, P.: Convex approximation technique for interacting line elements deblurring: a new approach. J. Math. Imaging Vis. 44(2), 168–184 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  7. Cricco, F., De Santi, G., Gerace, I.: A deterministic algorithm for deblurring considering higher order smoothness constraints. In: Proceedings of the 2003 International Workshop on Spectral Methods and Multirate Signal Processing SMMSP2004, Vienna, Austria, 11/12 September 2004, pp. 325–332 (2004)

    Google Scholar 

  8. Cricco, F., Gerace, I.: An IMAP estimation for the joint separation and restoration of mixed degraded color images. In: Proceedings of the 7th Conference on Applied and Industrial Mathematics in Italy, Venice (Italy), 20–24 September 2004, pp. 260–269 (2004)

    Google Scholar 

  9. Cluni, F., Costarelli, D., Minotti, A.M., Vinti, G.: Applications of sampling Kantorovich operators to thermographic images for seismic engineering. J. Comput. Anal. Appl. 19(4), 602–617 (2015)

    MathSciNet  MATH  Google Scholar 

  10. Discepoli, M., Gerace, I., Pandolfi, R.: Blind image restoration from multiple views by IMAP estimation. In: Modern Information Processing From Theory to Applications, pp. 441–452 (2006)

    Google Scholar 

  11. Evangelopoulos, X., Brockmeier, A. J., Mu, T., Goulermas, J. Y.: A graduated non-convexity relaxation for large-scale seriation. In: Proceedings of the 2017 SIAM International Conference on Data Mining, Houston, Texas (USA), pp. 462–470 (2017)

    Google Scholar 

  12. Fedeli, L., Gerace, I., Martinelli, F.: Unsupervised blind separation and deblurring of mixtures of sources. In: Proceedings of Knowledge-Based Intelligent Information and Engineering Systems. LNCS, Vietri sul Mare, Italy, vol. 4694, pp. 25–32 (2007)

    Google Scholar 

  13. Gerace, I., Cricco, F., Tonazzini, A.: An extended maximum likelihood approach for the robust blind separation of autocorrelated images from noisy mixtures. In: Puntonet, C.G., Prieto, A. (eds.) ICA 2004. LNCS, vol. 3195, pp. 954–961. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30110-3_120

    Chapter  Google Scholar 

  14. Gerace, I., Pandolfi, R.: A color image restoration with adjacent parallel lines inhibition. In: Proceedings of the 12th International Conference on Image Analysis and Processing, 17–19 September 2003, Mantova, Italy, 6 p. (2003)

    Google Scholar 

  15. Gerace, I., Pandolfi, R., Pucci, P.: A new estimation of blur in the blind restoration problem. In: Proceedings of the 2003 International Conference on Image Processing, Barcelona, Spain, 14–17 September 2003, pp. 261–264 (2003)

    Google Scholar 

  16. Gillis, N.: Sparse and unique nonnegative matrix factorization through data preprocessing. J. Mach. Learn. Res. 13, 3349–3386 (2012)

    MathSciNet  MATH  Google Scholar 

  17. Hazan, E., Levy, K.Y., Shalev-Shwartz, S.: On graduated optimization for stochastic non-convex problems. In: Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, vol. 48, pp. 1–9, 2016. JMLR: W & CP (2016)

    Google Scholar 

  18. Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10, 626–634 (1999)

    Article  Google Scholar 

  19. Hyvärinen, A.: Gaussian moments for noisy independent component analysis. IEEE Sig. Proc. Lett. 6, 145–147 (1999)

    Article  Google Scholar 

  20. Kuruoglu, E., Bedini, L., Paratore, M.T., Salerno, E., Tonazzini, A.: Source separation in astrophysical maps using independent factor analysis. Neural Netw. 16(3–4), 479–491 (2003)

    Article  Google Scholar 

  21. Leedham, G., Varma, S., Patankar, A., Govindaraju, V.: Separating text and background in degraded document images - a comparison of global thresholding techniques for multi-stage thresholding. In: Proceedings of the 8th International Workshop on Frontiers in Handwriting Recognition, Niagara on the Lake, Canada, pp. 244–249 (2002)

    Google Scholar 

  22. Liu, Z.-Y., Qiao, H.: GNCCP-graduated nonconvexity and concavity procedure. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1258–1267 (2014)

    Article  Google Scholar 

  23. Liu, Z.-Y., Qiao, H., Su, J.-H.: MAP inference with MRF by graduated non-convexity and concavity procedure. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8835, pp. 404–412. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12640-1_49

    Chapter  Google Scholar 

  24. Nikolova, M., Ng, M.K., Tam, C.-P.: On \(\ell _1\) data fitting and concave regularization for image recovery. SIAM J. Sci. Comput. 35(1), A397–A430 (2013)

    Article  MATH  Google Scholar 

  25. Nikolova, M., Ng, M.K., Zhang, S., Ching, W.-K.: Efficient reconstruction of piecewise constant images using nonsmooth nonconvex minimization. SIAM J. Imaging Sci. 1(1), 2–25 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  26. Rowley-Brooke, R., Pitié, F., Kokaram, A.: A ground truth bleed-through document image database. In: Zaphiris, P., Buchanan, G., Rasmussen, E., Loizides, F. (eds.) TPDL 2012. LNCS, vol. 7489, pp. 185–196. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33290-6_21

    Chapter  Google Scholar 

  27. Smith, T., Egeland, O.: Dynamical pose estimation with graduated non-convexity for outlier robustness. Model. Identif. Control (MIC) J. 43(2), 79–89 (2022)

    Article  Google Scholar 

  28. Tonazzini, A., Gerace, I., Martinelli, F.: Document image restoration and analysis as separation of mixtures of patterns: from linear to nonlinear models. In: Gunturk, B.K., Li, X. (eds.) Image Restoration - Fundamentals and Advances, pp. 285–310. CRC Press, Taylor & Francis, Boca Raton (2013)

    Google Scholar 

  29. Yang, H., Antonante, P., Tzoumas, V., Carlone, L.: Graduated non-convexity for robust spatial perception: from non-minimal solvers to global outlier rejection. IEEE Robot. Autom. Lett. 5(2), 1127–1134 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ivan Gerace .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Biondi, G., Boccuto, A., Gerace, I. (2023). Blind Source Separation of Color Noisy Blurred Images. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14108. Springer, Cham. https://doi.org/10.1007/978-3-031-37117-2_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37117-2_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37116-5

  • Online ISBN: 978-3-031-37117-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics