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Image Spectrometers, Color High Fidelity, and Fine-Art Paintings

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Abstract

This book chapter presents an introduction to image spectrometers with as example their application to the scanning of fine-art paintings. First of all, the technological aspects necessary to understand a camera as a measuring tool are presented. Thus, CFA-based cameras, Foveon-X, multi-sensors, sequential acquisition systems, and dispersing devices are introduced. Then, the simplest mathematical models of light measurement and light–matter interaction are described. Having presented these models, the so-called spectral reflectance reconstruction problem is presented. This problem is important because its resolution transforms a multi-wideband acquisition system into an image spectrometer. The first part of the chapter seeks to give the reader a grasp of how different technologies are used to generate a color image, and to which extent this image is expected to be high fidelity.

In a second part, a general view of the evolution of image spectrometers in the field of fine-art paintings scanning is presented. The description starts with some historical and important systems built during European Union projects, such as the pioneering VASARI or its successor CRISATEL. Both being sequential and filter-based systems, other sequential systems are presented, taking care to choose different technologies that show how a large variety of designs have been applied. Furthermore, a section about hyperspectral systems based on dispersing devices is included. Though not numerous and currently expensive, these systems are considered as the new high-end acquisition equipment for scanning art paintings. To finalize, some examples of applications such as the generation of underdrawings, virtual restoration of paintings or pigment identification are briefly described.

Until justice is blind to color, until education is unaware of race, until opportunity is unconcerned with the color of men’s skins, emancipation will be a proclamation but not a fact

Lyndon B. Johnson

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References

  1. Antonioli G, Fermi F, Oleari C, Riverberi R (2004) Spectrophotometric scanner for imaging of paintings and other works of art. In: Proceedings of CGIV, Aachen, Germany, 219–224

    Google Scholar 

  2. Asperen de Boer JRJVan (1968) Infrared reflectography: a method for the examination of paintings. Appl Optic 7:1711–1714

    Article  Google Scholar 

  3. Balas C, Papadakis V, Papadakis N, Papadakis A, Vazgiouraki E, and Themelis G (2003) A novel hyper-spectral imaging apparatus for the non-destructive analysis of objects of artistic and historic value. J Cult Herit 4(1):330–337

    Article  Google Scholar 

  4. Bayer BE (1976) Color imaging array. US Patent 3,971,065

    Google Scholar 

  5. Baronti S, Casini A, Lotti F and Porcinai S (1998) Multispectral imaging system for the mapping of pigments in works of art by use of principal component analysis. Appl Optic 37:1299–309

    Article  Google Scholar 

  6. Berns RS (2001) The science of digitizing paintings for color-accurate image archives. J Imag Sci Tech 45(4):305–325

    Google Scholar 

  7. Berns RS (2005) Rejuvenating the appearance of cultural heritage using color and imaging science techniques. In: Proceedings of the 10th Congress of the International Colour Association, 10th Congress of the International Colour Association, AIC Colour 05, Granada, Spain, 369–375

    Google Scholar 

  8. Berns RS, Taplin LA, Urban P, Zhao Y (2008) Spectral color reproduction of paintings. In: Proceedings CGIV 2008/MCS, 484–488

    Google Scholar 

  9. Burns PD (1997) Analysis of image noise in multitraitement color acquisition. Ph.D. Dissertation, Center for Imaging Science, Rochester Institute of Technology, Rochester, NY

    Google Scholar 

  10. Burmester A, Raffelt L, Robinson G, and Wagini S (1996) The MARC project: from analogue to digital reproduction. In: Burmester A, Raffelt L, Renger K, Robinson G and Wagini S (eds) Flämische Barockmalerei: Meisterwerke der alten Pinakothek München. Flemish Baroque painting: masterpieces of the Alte Pinakothek München. Hirmer Verlag, Munich, pp 19–26

    Google Scholar 

  11. Brauers J, Schulte N, and Aach T (2008) Multispectral filter-wheel cameras: geometric distortion model and compensation algorithms, IEEE Trans Image Process 17(12):2368-2380

    Article  MathSciNet  Google Scholar 

  12. Casini A, Bacci M, Cucci C, Lotti F, Porcinai S, Picollo M, Radicati B, Poggesi M and Stefani L (2005) Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi. In: Proceedings of SPIE 5857, 58570M doi:10.1117/12.611500. http://www.ifac.cnr.it/webcubview/WebScannerUK.htm

  13. Chang IC (1981) Acousto-optic tunable filters. Opt Eng 20:824–829

    Google Scholar 

  14. Carcagni P, Patria AD, Fontana R, Grecob M, Mastroiannib M, Materazzib M, Pampalonib E and Pezzatib L (2007) Multispectral imaging of paintings by optical scanning. Optic Laser Eng 45(3):360–367

    Article  Google Scholar 

  15. Clarke FJJ and Parry DJ (1985) Helmholtz reciprocity: its validity and application to reflectometry. Lighting Research and Technology 17(1):1–11

    Article  Google Scholar 

  16. Cupitt J and Martinez K (1996) VIPS: an image processing system for large images. In: Proceedings of IS&T/SPIE Symp. Electronic imaging: science and technology, very high resolution and quality imaging, vol 2663, pp 19–28

    Google Scholar 

  17. Cupitt J, Martinez K, and Saunders D (1996) A methodology for art reproduction in colour: the MARC project. Comput Hist Art 6(2):1–19

    Google Scholar 

  18. Dillon PLP, Lewis DM, Kaspar FG (1978) Color imaging system using a single CCD area array. IEEE J Solid State Circ 13(1):28–33

    Article  Google Scholar 

  19. Delaney JK, Zeibel JG, Thoury M, Littleton R, Palmer M, Morales KM, de la Rie ER, Hoenigswald A (2010) Visible and infrared imaging spectroscopy of picasso’s harlequin musician: mapping and identification of artist materials in situ. Appl Spectros 64(6):158A-174A and 563–689

    Google Scholar 

  20. Duncan DR (1940) The color of pigment mixtures. Proc of the phys soc 52:390

    Article  Google Scholar 

  21. Easton RL, Noel W (2010) Infinite possibilities: ten years of study of the archimedes palimpsest. Proc Am Phil Soc 154(1):50–76

    Google Scholar 

  22. Farrell JE, Wandell BA (1993) Scanner linearity. Journal of electronic imaging and color 3:147–161

    Google Scholar 

  23. Fischer C and Kakoulli I (2006) Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications. Rev Conserv 7:3–12

    Google Scholar 

  24. Gat N (2000) Imaging spectroscopy using tunable filters: a review. In Proceedings of SPIE, 4056:50–64

    Article  Google Scholar 

  25. Gilblom DL, Yoo SK, Ventura P (2003) Operation and performance of a color image sensor with layered photodiodes. Proc SPIE 5074:318–331

    Article  Google Scholar 

  26. Hadamard J (1902) Sur les problèmes aux dérivées partielles et leur signification physique. Bulletin University, Princeton, pp 49–52

    Google Scholar 

  27. Hansen PC (1998) Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion. SIAM, Philadelphia

    Book  Google Scholar 

  28. Haneishi H, Hasegawa T, Hosoi A, Yokoyama Y, Tsumura N, and Miyake Y (2000) System design for accurately estimating the spectral reflectance of art paintings. Appl Optic 39(35):6621–6632

    Article  Google Scholar 

  29. Hardeberg JY, Schmitt F, Brettel H, Crettez J, and Maître H (1999) Multispectral image acquisition and simulation of illuminant changes. In: MacDonald LW and Luo MR (eds) Color imaging: vision and technology. Wiley, New York, pp 145–164

    Google Scholar 

  30. Herzog P and Hill B (2003) Multispectral imaging and its applications in the textile industry and related fields. In: Proceedings of PICS03: The Digital Photography Conf., pp 258–263

    Google Scholar 

  31. Hubel PM, Liu J, Guttosch RJ (2004) Spatial frequency response of color image sensors: Bayer color filters and Foveon X3. Proceedings SPIE 5301:402–407

    Article  Google Scholar 

  32. Imai FH Rosen MR Berns RS (2001) Multi-spectral imaging of a van Gogh’s self-portrait at the National Gallery of Art, Washington, D.C. In: Proceedings of IS&T Pics Conference, IS&T, PICS 2001: image processing, image quality, image capture systems conference, Rochester, NY, USA, pp 185–189

    Google Scholar 

  33. Imai FH, Taplin LA, and Day EA (2002) Comparison of the accuracy of various transformations from multi-band image to spectral reflectance. Tech Rep, Rochester Institute of Technology, Rochester, NY

    Google Scholar 

  34. Kubelka P and Munk F (1931).“Ein beitrag zur optik der farbanstriche”, Zurich Tech., Physik 12:pp. 543.

    Google Scholar 

  35. Kubelka P (1948) New contributions to the optics of intensely light-scattering materials, part I. J Opt Soc Am 38:448–460

    Article  MathSciNet  Google Scholar 

  36. Keusen T (1996) Multispectral color system with an encoding format compatible with the conventional tristimulus model. J Imag Sci Tech 40(6):510–515

    Google Scholar 

  37. König F and Praefcke W (1999) A multispectral scanner. Chapter in MacDonald and Luo. pp 129–144

    Google Scholar 

  38. Lenz R (1990) Calibration of a color CCD camera with 3000x2300 picture elements. In: Proceeding of Close Range Photogrammetry Meets Machine Vision, Zurich Switzerland, 3–7 Sept 1990. Proc SPIE, 1395:104–111 ISBN: 0–8194–0441–1

    Google Scholar 

  39. Liang H, Saunders D, and Cupitt J (2005) A new multispectral imaging system for examining paintings. J Imag Sci Tech 49(6):551–562

    Google Scholar 

  40. Maître H, Schmitt F, Crettez J-P, Wu Y and Hardeberg JY (1996) Spectrophotometric image analysis of fine art paintings. In: Proc. of the Fourth Color Imaging Conference, Scottsdale, Arizona, pp 50–53

    Google Scholar 

  41. Malzbender T, Gelb D, Wolters H (2001) Polynomial texture maps. In: SIGGRAPH: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, ACM press, New york, NY, USA, pp 519–528

    Google Scholar 

  42. Martinez K, Cupitt J, Saunders D, and Pillay R (2002) Ten years of art imaging research. Proc IEEE 90:28–41

    Article  Google Scholar 

  43. Miao L and Qi HR (2006) The design and evaluation of a generic method for generating mosaicked multispectral filter arrays. IEEE Trans Image Process 15(9):2780–2791

    Article  Google Scholar 

  44. Nicodemus FE, Richmond JC, Hsia JJ, Ginsberg IW, Limperis T (1977) Geometrical considerations and nomenclature for reflectance. US Department of Commerce, National Bureau of Standards

    Google Scholar 

  45. Novati G, Pellegri P, and Schettini R (2005) An affordable multispectral imaging system for the digital museum. Int J Dig Lib 5(3): 167–178

    Article  Google Scholar 

  46. Okano Y (1995) Electronic digital still camera using 3-CCD image sensors. In: Proceedings of IS&T’s 48th Annual Conf., 428–432

    Google Scholar 

  47. Pappas M and Pitas I (2000) Digital color restoration of old paintings. Trans Image Process (2):291–294

    Article  Google Scholar 

  48. Parkkinen JPS, Hallikainen J, and Jaaskelainen T (1989) Characteristic spectra of Munsell color. J Opt Soc Am 6:318–322

    Article  Google Scholar 

  49. Paviotti A, Ratti F, Poletto L, and Cortelazzo GM (2009) Multispectral acquisition of large-sized pictorial surfaces. EURASIP Int J Image Video Process Article ID 793756, 17

    Google Scholar 

  50. Pelagotti A, Mastio AD, Rosa AD, and Piva A (2008) Multispectral imaging of paintings. IEEE Signal Processing Mag 25(4):27–36

    Article  Google Scholar 

  51. Pillay R (2011) Hyperspectral imaging of paintings, web article accessed on October http://merovingio.c2rmf.cnrs.fr/technologies/?q=hyperspectral

  52. Pratt WK and Mancill CE (1976) Spectral estimation techniques for the spectral calibration of a color image scanner. Appl Opt 15(1):73–75

    Article  Google Scholar 

  53. Ribés A, and Schmitt F (2003) A fully automatic method for the reconstruction of spectral reflectance curves by using mixture density networks. Pattern Recogn Lett 24(11):1691–1701

    Article  Google Scholar 

  54. Ribés A, Schmitt F, Pillay R, and Lahanier C (2005) Calibration, spectral reconstruction and illuminant simulation for CRISATEL: an art paint multispectral acquisition system. J Imaging Sci Tech 49(6):463–473

    Google Scholar 

  55. Ribés A, Pillay R, Schmitt F, and Lahanier C (2008) Studying that smile: a tutorial on multispectral imaging of paintings using the Mona Lisa as a case study. IEEE Signal Process Mag 25(4):14–26

    Article  Google Scholar 

  56. Ribés A and Schmitt F (2008) Linear inverse problems in imaging. IEEE Signal Process Mag 25(4):84–99

    Article  Google Scholar 

  57. Rush A, Hubel PM (2002) X3 sensor characteristics Technical Repport, Foveon, Santa Clara, CA

    Google Scholar 

  58. Saunders D, and Cupitt J (1993) Image processing at the National Gallery: the VASARI project. National Gallery Technical Bulletin 14:72–86

    Google Scholar 

  59. Saunders D (1998) High quality imaging at the National Gallery: origins, implementation and applications. Comput Humanit 31:153–167

    Article  Google Scholar 

  60. Sharma G, Trussell HJ (1997) Digital color imaging. IEEE Trans on Image Process 6(7): 901–932

    Article  Google Scholar 

  61. Shimano N (2006) Recovery of spectral reflectances of objects being imaged without prior knowledge. IEEE Trans Image Process 15:1848–1856

    Article  Google Scholar 

  62. Tominaga S, Tanaka N (2008) Spectral image acquisition, analysis, and rendering for art paintings J Electron Imaging 17:043022

    Google Scholar 

  63. Wyszecki G and Stiles WS (2000) Color science: concepts and methods, quantitative data and formulae. John Wiley and Sons, 2nd edition

    Google Scholar 

  64. Wright W (1981) A mobile spectrophotometer for art conservation Color Res Appl 6:70–74

    Google Scholar 

  65. Zhao Y and Berns RS (2007) Image-based spectral reflectance reconstruction using the matrix R method. Color Res Appl 32:343–351. doi: 10.1002/col.20341

    Article  Google Scholar 

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Acknowledgments

I would like to thank Ruven Pillay for providing information on the VASARI project and on the C2RMF hyperspectral imaging system; as well as for having corrected and proof-read parts of the manuscript. Thanks also to Morwena Joly for the photograph of the transmission grating-based scanner recently acquired by the Centre de Restauration des Musees de France. I also extend my sincere thanks to: the Département des peintures of Musée du Louvre, for permission to use the images of the Mona Lisa; Lumière Technologie for the images of the CRISATEL camera and its filters; and Kirk Martinez for making available the images of the VASARI project.

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Ribés, A. (2013). Image Spectrometers, Color High Fidelity, and Fine-Art Paintings. In: Fernandez-Maloigne, C. (eds) Advanced Color Image Processing and Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6190-7_14

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  • DOI: https://doi.org/10.1007/978-1-4419-6190-7_14

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