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Sensing and Imaging

, 20:13 | Cite as

Quality Evaluation of RGB Images Reconstructed by Means of Photoacoustic Signals

  • Lorenzo Miris
  • Enrico Vannacci
  • Simona GranchiEmail author
  • Elena Biagi
Original Paper
  • 17 Downloads

Abstract

Recent researches have demonstrated the usefulness of photoacoustics in non-destructive control, in particular, in the monitoring and diagnosis of works of art. Indeed, it is fundamental to preserve the artworks’ integrity by using techniques not involving direct contact or damaging radiation, or pre-treatments. On the other hand, a lot of artistic heritage consists of paintings that are complex systems, where, often, the presence of highly scattering and semi-opaque materials make useless optical techniques. Consequently, in this context photoacoustics represent a powerful tool. This work is aimed to evaluate the quality of reconstructed RGB images of simple test objects examined by means of photoacoustic signals, in order to confirm the potentiality of this promising investigation method. Only a single-wavelength excitation source at 1064 nm was available and so, it has been necessary to perform some preliminary processings on the sample color images. The original images have been decomposed in R, G and B components; each of them has been converted into grayscale code, printed on transparency film and then investigated through photoacoustics. After that, the three generated photoacoustic images have been recombined to produce the reconstructed RGB image. A complete experimental system has been set to analyse dedicated test objects. The resulting images have been compared to the original ones, by using standard image quality parameters. Similar results are expected to be obtained by using three sources of distinct wavelengths (Red, Green, Blue), making the method easier to apply.

Keywords

Photoacoustics Image processing RGB image reconstruction Artwork diagnosis Artistic heritage monitoring Non destructive control 

Notes

Acknowledgements

Authors desire to give a special thanks to FONDAZIONE CASSA DI RISPARMIO DI PISTOIA E PESCIA (Pistoia, Italy) that has kindly concessed the use of the trademarks adopted in photoacoustic image reconstruction experimentation.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Wang, T., Nandy, S., Salehi, H. S., Kumavor, P. D., & Zhu, Q. (2014). A low-cost photoacoustic microscopy system with a laser diode excitation. Biomedical Optics Express, 5(9), 3053–3058.  https://doi.org/10.1364/BOE.5.003053.CrossRefGoogle Scholar
  2. 2.
    Liang, Y., Jin, L., Wang, L., Bai, X., Cheng, L., & Guan, B.-O. (2017). Fiber-laser-based ultrasound sensor for photoacoustic imaging. Scientific Reports.  https://doi.org/10.1038/srep40849.CrossRefGoogle Scholar
  3. 3.
    Taruttis, A., & Ntziachristos, V. (2015). Advances in real-time multispectral optoacoustic imaging and its applications. Nature Photonics, 9(4), 219.  https://doi.org/10.1038/nphoton.2015.29.CrossRefGoogle Scholar
  4. 4.
    Ntziachristos, V., Ripoll, J., Wang, L. V., & Weissleder, R. (2005). Looking and listening to light: The evolution of whole-body photonic imaging. Nature Biotechnology, 23(3), 313–320.  https://doi.org/10.1038/nbt1074.CrossRefGoogle Scholar
  5. 5.
    Xia, J., Yao, J., & Wang, L. V. (2014). Photoacoustic tomography: Principles and advances. Electromagnetic Waves (Cambridge, Mass.), 147, 1–22.Google Scholar
  6. 6.
    Wang, S., Tao, C., Yang, Y., Wang, X., & Liu, X. (2015). Theoretical and experimental study of spectral characteristics of the photoacoustic signal from stochastically distributed particles. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 62(7), 1245–1255.  https://doi.org/10.1109/TUFFC.2014.006806.CrossRefGoogle Scholar
  7. 7.
    Patterson, M. P., Riley, C. B., Kolios, M. C., & Whelan, W. M. (2011). Optoacoustic signal amplitude and frequency spectrum analysis laser heated bovine liver ex vivo. In IEEE International Ultrasonics Symposium Proceedings, Conference Proceedings (pp. 300–303).  https://doi.org/10.1109/ultsym.2011.0072.
  8. 8.
    Cox, B., Laufer, J. G., Arridge, S. R., & Beard, P. C. (2012). Quantitative spectroscopic photoacoustic imaging: A review. Journal of Biomedical Optics, 17(6), 061202.  https://doi.org/10.1117/1.JBO.17.6.061202.CrossRefGoogle Scholar
  9. 9.
    Cox, B. T., Arridge, S. R., & Beard, P. C. (2009). Estimating chromophore distributions from multiwavelength photoacoustic images. Journal of the Optical Society of America A: Optics, Image Science, and Vision, 26(2), 443–455.CrossRefGoogle Scholar
  10. 10.
    Bayer, C. L., Wlodarczyk, B. J., Finnell, R. H., & Emelianov, S. Y. (2017). Ultrasound-guided spectral photoacoustic imaging of hemoglobin oxygenation during development. Biomedical Optics Express, 8(2), 757.  https://doi.org/10.1364/BOE.8.000757.CrossRefGoogle Scholar
  11. 11.
    Tserevelakis, G. J., Dal Fovo, A., Melessanaki, K., Fontana, R., & Zacharakis, G. (2018). Photoacoustic signal attenuation analysis for the assessment of thin layers thickness in paintings. Journal of Applied Physics, 123(12), 123102.  https://doi.org/10.1063/1.5022749.CrossRefGoogle Scholar
  12. 12.
    Tserevelakis, G. J., Tsagkaraki, M., Siozos, P., & Zacharakis, G. (2018). Uncovering the hidden content of layered documents by means of photoacoustic imaging. Strain.  https://doi.org/10.1111/str.12289.CrossRefGoogle Scholar
  13. 13.
    Billeh, Y. N., Liu, M., & Buma, T. (2010). Spectroscopic photoacoustic microscopy using a photonic crystal fiber supercontinuum source. Optics Express, 18(18), 18519–18524.CrossRefGoogle Scholar
  14. 14.
    Dogra, V. S., Chinni, B. K., Valluru, K. S., Moalem, J., Giampoli, E. J., Evans, K., et al. (2014). Preliminary results of ex vivo multispectral photoacoustic imaging in the management of thyroid cancer. AJR. American Journal of Roentgenology, 202(6), W552–W558.  https://doi.org/10.2214/AJR.13.11433.CrossRefGoogle Scholar
  15. 15.
    Vannacci, E., Granchi, S., Biagi, E., Belsito, L., & Roncaglia, A. (2015). High resolution ultrasonic images by miniaturized fiber-optic probe. Lecture Notes in Electrical Engineering, 319, 349–353.  https://doi.org/10.1007/978-3-319-09617-9_61.CrossRefGoogle Scholar
  16. 16.
    Vannacci, E., Granchi, S., Belsito, L., Roncaglia, A., & Biagi, E. (2017). Wide bandwidth fiber-optic ultrasound probe in MOMS technology: Preliminary signal processing results. Ultrasonics, 75, 164–173.  https://doi.org/10.1016/j.ultras.2016.11.024.CrossRefGoogle Scholar
  17. 17.
    Vannacci, E., Belsito, L., Mancarella, F., Ferri, M., Veronese, G. P., Roncaglia, A., et al. (2014). Miniaturized fiber-optic ultrasound probes for endoscopic tissue analysis by micro-opto-mechanical technology. Biomedical Microdevices, 16(3), 415–426.  https://doi.org/10.1007/s10544-014-9844-6.CrossRefGoogle Scholar
  18. 18.
    Biagi, E., Cerbai, S., Masotti, L., Belsito, L., Roncaglia, A., Masetti, G., & Speciale, N. (2010). MOMS technology for fully fiber optic ultrasonic probes: A proposal for virtual biopsy. In 2010 IEEE Sensors (pp. 1156–1160).  https://doi.org/10.1109/ULTSYM.2009.5441697.
  19. 19.
    Biagi, E., Cerbai, S., Masotti, L., Belsito, L., Roncaglia, A., Masetti, G., & Speciale, N. (2009). Fiber optic broadband ultrasonic probe for virtual biopsy: Technological solutions. In 2009 IEEE International Ultrasonics Symposium (IUS) (pp. 200–203).  https://doi.org/10.1109/ULTSYM.2009.5441697.
  20. 20.
    Biagi, E., Cerbai, S., Gambacciani, P., Acquafresca, A., & Masotti, L. (2006). Fully fiber optic ultrasonic probes for virtual biopsy. In Presented at the proceedings—IEEE ultrasonics symposium (Vol. 1, pp. 556–559).  https://doi.org/10.1109/ultsym.2006.144.
  21. 21.
    Belsito, L., Mancarella, F., Ferri, M., Roncaglia, A., Biagi, E., Cerbai, S., et al. (2011). Micro-Opto-Mechanical technology for the fabrication of highly miniaturized fiber-optic ultrasonic detectors. In Presented at the 2011 16th international solid-state sensors, actuators and microsystems conference, TRANSDUCERS’11 (pp. 594–597).  https://doi.org/10.1109/transducers.2011.5969725.
  22. 22.
    Belsito, L., Vannacci, E., Mancarella, F., Ferri, M., Veronese, G., Biagi, E., et al. (2014). Fabrication of fiber-optic broadband ultrasound emitters by micro-opto-mechanical technology. Journal of Micromechanics and Microengineering.  https://doi.org/10.1088/0960-1317/24/8/085003.CrossRefGoogle Scholar
  23. 23.
    Gogoi, M., & Ahmed, M. (2016). Image quality parameter detection: A study. International Journal of Computer Sciences and Engineering, 4, 7.Google Scholar
  24. 24.
    Al-Najjar, Y. A. Y., & Soong, D. C. (2012). Comparison of image quality assessment: PSNR, HVS, SSIM, UIQI. International Journal of Scientific Engineering and Research, 3(8), 1–5.Google Scholar
  25. 25.
    Eskicioglu, A. M., & Fisher, P. S. (1995). Image quality measures and their performance. IEEE Transactions on Communications, 43(12), 2959–2965.  https://doi.org/10.1109/26.477498.CrossRefGoogle Scholar
  26. 26.
    Ahmed, M., & Tech, M. (2017). An effective image quality estimation method for color image. Mathematical Sciences, 6(8), 7.Google Scholar
  27. 27.
    Study of the Conditions of Irradiating Laser for Removal of Toner from Used Paper. (2009). Retrieved January 29, 2019, from https://www.jstage.jst.go.jp/article/ieejfms/129/4/129_4_205/_article/-char/en.
  28. 28.
    Leal-Ayala, D. R., Allwood, J. M., Schmidt, M., & Alexeev, I. (2012). Toner-print removal from paper by long and ultrashort pulsed lasers. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 468(2144), 2272–2293.  https://doi.org/10.1098/rspa.2011.0601.CrossRefGoogle Scholar
  29. 29.
    Counsell, T. A. M., & College, C. (2007). Remove toner: Reuse paper, 188.Google Scholar
  30. 30.
    Emelianov, S., Aglyamov, S., Karpiouk, A., Mallidi, S., Park, S., Sethuraman, S., et al. (2006). Synergy and applications of combined ultrasound, elasticity, and photoacoustic imaging. In 2006 IEEE international ultrasonics symposium, IUS (pp. 405–415). Presented at the 2006 IEEE International Ultrasonics Symposium, IUS.  https://doi.org/10.1109/ultsym.2006.114.
  31. 31.
    Wang, Z., & Sheikh, H. R. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4), 14.CrossRefGoogle Scholar
  32. 32.
    Yao, J., & Wang, L. V. (2014). Sensitivity of photoacoustic microscopy. Photoacoustics, 2(2), 87–101.  https://doi.org/10.1016/j.pacs.2014.04.002.CrossRefGoogle Scholar
  33. 33.
    Pinki, R. M. (2016). Estimation of the image quality under different distortions. International Journal of Engineering and Computer Science, 5(7), 17291–17296.Google Scholar
  34. 34.
    Jagalingam, P., & Hegde, A. V. (2015). A review of quality metrics for fused image. Aquatic Procedia, 4, 133–142.  https://doi.org/10.1016/j.aqpro.2015.02.019.CrossRefGoogle Scholar
  35. 35.
    (12) IMAGE QUALITY PARAMETERS: A short review and applicability analysis. (2016). ResearchGate. Retrieved July 17, 2018, from https://www.researchgate.net/publication/308995206_IMAGE_QUALITY_PARAMETERS_A_short_review_and_applicability_analysis.
  36. 36.
    Baglioni, P., Berti, D., Bonini, M., Carretti, E., Dei, L., Fratini, E., et al. (2014). Micelle, microemulsions, and gels for the conservation of cultural heritage. Advances in Colloid and Interface Science, 205, 361–371.  https://doi.org/10.1016/j.cis.2013.09.008.CrossRefGoogle Scholar
  37. 37.
    Baglioni, P., Carretti, E., & Chelazzi, D. (2015). Nanomaterials in art conservation. Nature Nanotechnology, 10, 287–290.  https://doi.org/10.1038/nnano.2015.38.CrossRefGoogle Scholar
  38. 38.
    Mazzuca, C., Bocchinfuso, G., Cacciotti, I., Micheli, L., Palleschi, G., & Palleschi, A. (2010). Versatile hydrogels: An efficient way to clean paper artworks. RSC Advances, 3(45), 22896–22899.  https://doi.org/10.1039/C3RA44387F.CrossRefGoogle Scholar
  39. 39.
    New Frontiers in Materials Science for Art Conservation: Responsive Gels and Beyond—Accounts of Chemical Research (ACS Publications). (2010). Retrieved February 8, 2019, from https://pubs.acs.org/doi/abs/10.1021/ar900282h.
  40. 40.
    Tserevelakis, G. J., Vrouvaki, I., Siozos, P., Melessanaki, K., Hatzigiannakis, K., Fotakis, C., et al. (2017). Photoacoustic imaging reveals hidden underdrawings in paintings. Scientific Reports, 7(1), 747.  https://doi.org/10.1038/s41598-017-00873-7.CrossRefGoogle Scholar
  41. 41.
    Siddiolo, A. M., D’Acquisto, L., Maeva, A. R., & Maev, R. G. (2007). Wooden panel paintings investigation: An air-coupled ultrasonic imaging approach. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 54(4), 836–846.  https://doi.org/10.1109/TUFFC.2007.317.CrossRefGoogle Scholar
  42. 42.
    Siddiolo, A. M., Maeva, A., & Maev, R. G. (2007). Air-coupled imaging method applied to the study and conservation of paintings. In M. P. André, I. Akiyama, M. Andre, W. Arnold, J. Bamber, V. Burov, et al. (Eds.), Acoustical imaging (pp. 3–12). Berlin: Springer.CrossRefGoogle Scholar
  43. 43.
    Hochreiner, A., Bauer-Marschallinger, J., Burgholzer, P., Jakoby, B., & Berer, T. (2013). Non-contact photoacoustic imaging using a fiber based interferometer with optical amplification. Biomedical Optics Express, 4(11), 2322–2331.  https://doi.org/10.1364/BOE.4.002322.CrossRefGoogle Scholar
  44. 44.
    Wang, Y., Li, C., & Wang, R. K. (2011). Noncontact photoacoustic imaging achieved by using a low-coherence interferometer as the acoustic detector. Optics Letters, 36(20), 3975–3977.  https://doi.org/10.1364/OL.36.003975.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Ultrasound and Non-Destructive Testing Lab, Department of Information Engineering (DINFO)University of FlorenceFlorenceItaly

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