SPECTRAL AND COLORIMETRIC CHARACTERIZATION OF PAINTED SURFACES: A SCANNING DEVICE FOR THE IMAGING ANALYSIS OF PAINTINGS P. Carcagnì1, A. Della Patria1 , R. Fontana2, M. Greco2, M. Mastroianni2, M. Materazzi2, E. Pampaloni2, L. Pezzati2, R. Piccolo1 1
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Summary In the last few years the multispectral imaging has been widely used as a tool for paintings diagnostics and conservation because of its effectiveness and safety. It allows spectral and colorimetric characterization of the painted surface and it is used to document the conservation state of the artwork as well as to identify the materials composing the painted surface. In this work we present a scanning system for 32-band multispectral imaging of paintings in the 380÷800 nm spectral region. This system is based on a fast spectrometer for contact-less single-point measures mounted on two orthogonal XY translation stages. It can scan an area of 1 m2 with a spatial resolution of 4 dots/mm and a spectral resolution of 10 nm. An adjustment procedure has been established and the calibration has been performed by means of a set of 7 matt ceramic colour standard certificated by the National Physical Laboratory (UK). As examples of application we present the results of measurements performed on different ancient paintings: “Portrait of Lionello d’Este” by Pisanello, “Croce di Rosano” by Maestro di Rosano, and a fresco dated between the VI and the VIII century, at “Santa Maria Antiqua” Church in Rome.
1.Introduction Imaging techniques provide the characterization of a volume or a surface by measuring one or more physical parameters (colorimetric coordinates, spectral reflection factor, quote, … ) with a high-density and spatially-referenced sampling of the object as a whole. Measurement results are then displayed by means of a set of images each concerning one of the above mentioned parameters. Multispectral imaging, in particular, is the spectral reflection factor (R) characterization of a painted surface in the visible (VIS) and near infrared (NIR) spectral range. Spectral reflectance and colorimetric characterization has been extremely well received for ancient painting analysis because of its several applications ranging from diagnostics (e.g. identification of materials composing the painted surface) to digital documentation and monitoring of the conservation state of an artwork, in an absolutely non-destructive way [1-4]. The technique is based on irradiating the painting surface with broadband continuous sources, such as halogen lamps, and detecting with a suitable device the back-scattered radiation within narrow spectral intervals (few tens of nanometers as a maximum) covering the spectral range of interest. Up to now, either CCD or Vidicon cameras have been used as a detector [4-8]. Both ways of detection entail calibration procedures to correct for non-uniform irradiation of the investigated area as well as for geometrical distortion due to the camera lens and to the device intrinsic characteristics. Moreover, as the spatial resolution is related to the measured area, in case of large panels several images must be collected and then combined into a mosaic. To overcome the above mentioned problems, related to the use of spatially-extended sensors, we propose a innovative device based on a fast single-point spectrometer, specifically developed for multispectral imaging in the 380-800 nm spectral region, opportunely combined with an high-resolution scanning system. A correct adjustment procedure, that makes use of a spectral reflection reference tile, allows to measure R for each pixel of the acquired image; from these data can be calculated the colorimetric coordinates for the whole painting surface. The sequence of acquired images (one for each spectral interval), properly processed by means of multivariate analysis techniques, can be a powerful tool for painting diagnostics [4]. We report on the characteristics of the device showing also the established adjustment and calibration procedures. Spectral reflection factor and colorimetric coordinates measurements were carried out on coloured ceramic tiles and the results were compared with the corresponding values, certified by the National Physical Laboratory (NPL) of UK.
We also present the results of measurements performed on different ancient paintings.
2. The multispectral scanner The block diagram of the device is shown in Fig. 1: two orthogonally-mounted motorized translation
stages
move
the
lighting
system
and
the
collecting
optics.
The
illumination/observation geometry is the standard 45°/0°, suggested by CIE for reflectance measurements [9]. The collecting optics gathers the radiation scattered from the scanned point on the painting and focuses it on the end of a multimode optical fibre that carries the light to the sensitive surface of the detector. The detection system is made of a 32-photomultiplier (PM) array, each element being filtered to select different 10 nm-wide (FWHM) bands in the 380-800 nm spectral range. Multispectral imaging of up to 1 m2 surfaces with a spatial resolution of 4 dots/mm is achievable. Fibre light guiding minimizes the load on the scanning system, thus limited to the source and the collecting optics. The simultaneous motion of the lighting system and the collecting optics limits surface heating and, together with point-by-point surface sampling, assures uniform lighting. Moreover, single point detection entails off-axis -aberration free images. The lighting system (Fig.2) is composed of two low-voltage current-stabilized halogen lamps irradiating a 5 cm2 area. The reflectors have a hard dichroic coating that transmits backwards large part of the generated IR radiation, thus minimizing surface heating. The beam divergence is 10°, according to CIE specifications for the 45°/0° illumination/observation geometry. In order to avoid chromatic aberration, the collecting optics (Fig. 2) is composed of two faced spherical mirrors. The working entrance-f# is 2.4, corresponding to an acceptance angle of 24°. The working exit-f# is 3.6 to obtain best matching with the multimode optical fibre (N.A. = 0.22, ∅core = 200 µm). The 1.5 magnification factor entails a scanned point diameter of about 130 µm on the painted surface, thus letting a margin of more than 100 µm before exceeding the sampling step (250 µm), i.e. before the information concerning two adjacent points overlaps. Optical simulations were carried out to determine the tolerance on the distance between the collecting optics and the painting surface, finding out a depth of field of ±250 µm. The detector is a linear multi-anode photomultiplier (customized version of H7260-01FSEL, Hamamatsu) composed of 32 elements with an effective area of 0.8×7 mm2. The photocathode is equipped with an array of 32 interferential filters with 10 nm FWHM
bandwidth, covering the 380-800 nm spectral range. The spacing of filter central wavelengths is 10 or 20 nm, being smaller in the 400-600 nm spectral region where the variability of the spectra of painting materials is maximum. The choice of a detector with high sampling frequency (up to 5 KHz) and high anode sensitivity (the gain ranges from 102 to 106) is due to the characteristic low reflectance of pictorial materials (2-5 %) together with the requirement of minimizing the radiation on the painted surface and the acquisition time. The 12 bit A/D conversion and the low noise level enables tonal dynamics of about 4000 grey levels.
3. The XY scanning system As the instrument is supposed to move for in situ measurements, the XY scanning system (Fig. 3), composed of high-precision motorized translation stages, can be dismantled in separate modules whose maximum weight is 30 kg. Mechanical joints and interlocking systems ease their assembly. The whole system is computer controlled: acquisition parameters (scanned area, integration time, detector gain, sampling frequency, …) are adjustable depending on the sampled surface characteristics. The digitised data are recorded and handled via a dedicated software running on a standard PC and can be displayed as 32 monochromatic images. Storing the images in a 8-bit standard format can be chosen either to convert all the grey scale, thus compressing the intensity resolution, or to convert a limited part of the grey scale, to have the highest intensity resolution in selected parts of the image.
4. Calibration and adjustment For a specific illumination/observation geometry, the spectral reflection factor Rλsample is calculated according to the following expression: Rλsample = ρ λreference
Vλsample − Vλdark Vλreference − Vλdark
Eq. (1)
where ρλreference is the certified spectral reflectance of a neutral diffuser (we usually use a neutral diffuser with mean spectral reflectance of 66%, certified by the National Physical Laboratory (NPL), UK) in the specified geometry, Vλsample and Vλreference are the detector signals concerning the scattered radiation by the sampled point and by the neutral diffuser respectively and Vλdark is the detector signal when the collecting optics entrance is darkened. Vλreference and Vλdark detection is the instrument adjustment procedure, that has to be repeated before starting each measurement session.
The calibration procedure, as determination of the instrument accuracy, was accomplished by measuring the spectral reflection factor of seven coloured ceramic tiles of NPL and comparing the results to the corresponding certified values. Each acquisition was repeated 100 times to evaluate measurement repeatability. In order to obtain a quantitative estimate of instrument accuracy, the mean absolute deviation ∆R was calculated for each tile after 1 nm interpolation of the corresponding measured and certified spectra (see Table 1). In Fig.4 the measured spectral reflection factors (colour circles) and the respective certified values (black squares) are shown on the same graph. Experimental points are associated with the standard deviation of the mean. Good agreement between the two data sets is present even in case of low spectral reflection factor and spectrum slope variation. Colorimetric calculations were also carried out in accordance with Eq. (2),
780
X =K
∑ Sλ Rλ x λ ∆λ λ = 380 780
Y =K
∑ Sλ Rλ y λ ∆λ λ =380 780
Z=K
∑
S λ Rλ z λ ∆λ
Eq. (2)
λ = 380
K=
100 780
∑ S λ y λ ∆λ λ =380
where Sλ is the spectral distribution of the standard D65 illuminant, x λ , y λ , z λ are the colorimetric functions for the standard observer CIE 1931 and Rλ is the 1nm-interpolated experimental spectral reflection factor. Finally (see Table 2) the chromaticity coordinates x, y, defined by Eq. (3), and the luminance Y (Eq. (2)) were compared to the NPL certified values.
X X +Y + Z Y y= X +Y + Z
x=
Eq. (3)
5. Applications The scanning spectrometer was tested on different ancient paintings: “Croce di Rosano” by Maestro di Rosano, “Portrait of Lionello d’Este” by Pisanello, and a fresco dated between the
VI and the VIII century, at “Santa Maria Antiqua” Church in Rome. Below we show a few results. The imaging analysis of “Croce di Rosano”, a wooden crucifix dated XII century, now under repair at the “Opificio delle Pietre Dure”, was carried out on a detail (60x16 cm2) of the painting. The results of the acquisition can be visualized as 32 monochromatic pictures, one for each spectral band; some of these, which show a detail (the face of the angel) of the acquired area, are depicted in Fig. 5. Colorimetric coordinates in the XYZ colour space were computed for each pixel of the sampled area, and than they were converted into the RGB coordinates. After normalization to the highest of the RGB values, the resulting data were stored as standard format 8-bit images and finally the three images, R-G-B were combined to get the colour image (Fig. 6) of the sampled area. Fig.7 shows another detail, representing the three Mary’s at Christ’s sepulchre; from top to bottom are displayed the RGB colour image, the IR@800 nm image and a false colour image. The first one was obtained by properly mixing the 31 monochromatic images; the second one is the IR reflectogram collected on the 32nd channel (which, although it is characterised by lesser legibility than the Vidicon or IR scanner by INOA reflectography, provides an information equal to that obtained with the photographic technique) and the last one is the false colour image obtained through the combination of the IR, R, G images attributed to the R, G, B channels, respectively. The latter image allows to highlight the difference between pigments having the same chromatic yield and being thus indistinguishable at sight. This is the case, e.g., of the lapis lazuli and azurite which are identifiable only with the false colour image, with the lapis lazuli appearing red. Considering the RGB image (top) and the false colour (bottom), the veil of the two outer women is probably lapis lazuli. In order to confirm this data, spectral characterization of the detail was useful. The spectral reflection factor reported in fig. 7 is an average value over 1000 adjacent points, in order to consider spectra that would be representative of the measured area. The spectra were then compared with spectral reflection factors of samples, purposely prepared at the Opificio delle Pietre Dure restoration laboratory in Florence, whose composition reproduces ancient painting materials. The R values of the samples were measured with a commercial contact spectrometer characterized by the same illumination/observation geometry as ours.
The comparison of painting and reference spectral reflection factors showed a good agreement between the veil’s pigment and lapis lazuli-carmine mixture. These preliminary results show how multispectral analysis can be an effective tool not only for painting documentation and monitoring but also for pigment identification. Another important application concerns the Pisanello’s “Portrait of Lionello d’Este”. The portrait of Lionello d’Este is one of the most famous paintings of the Italian Renaissance because of its dynastic and profane imagery. It was painted in a very particular and rich technique, using many glazes and metal leaves and probably an oil medium for the flesh tones (which is very unusual in Italy in the period). The results obtained with the multispectral scanner are displayed in fig.8 where on the same picture were combined (from left to right), the multispectral colour imaging, the IR reflectography (acquired with IR reflectography scanner by INOA), and the false colour image. Finally we present the results obtained with the multispectral analysis on a fresco at “Santa Maria Antiqua” Church in Rome. Fig.9 shows the comparison between the RGB colour image and a digital photograph of the same detail representing the scene of a man’s torture. The high spatial resolution and the high colour definition allow for an extremely detailed reproduction of the surface. Moreover, comparison by visual inspection of the colour image with the original fresco showed a high fidelity reproduction in the chromatic yield. What’s most relevant is that the colour image obtained with the scanning spectrometer is more than a simple colour picture of the artwork. It is in fact a high spatial resolution, distortionfree colour measure of the painting surface, that can be displayed as a colour image in 1:1 scale with the original artwork.
6.Conclusions A scanning spectrometer for multispectral and colorimetric characterization of painting surfaces was developed. The device is suited to multispectral imaging in the 380-800 nm spectral region of surfaces up to 1 m2 with spatial resolution of 4 dots/mm and spectral resolution of 10 nm. The spectrometer accuracy was determined by measuring spectral reflection factor and colorimetric coordinates on a set of coloured ceramic tiles, certified by the NPL in UK. We have presented the results of measurements performed on different ancient paintings: “Portrait of Lionello d’Este” by Pisanello, “Croce di Rosano” by Maestro di Rosano, and a
fresco dated between the VI and the VIII century, at “Santa Maria Antiqua” Church in Rome. These results showed the usefulness of the developed instrument for monitoring and digital documentation of the conservation state of works of art. Multispectral imaging application to ease pigment identification was also shown. Our next goals are the extension of the spectral sensitivity up to 2400 nm and the introduction of an autofocus system that, besides keeping the painting surface focussed during the scan even in case of surface irregularities, will allow the simultaneous acquisition of the painting shape.
References [1] Maitre H, Schmitt F, Crettez J, Wu Y, Hardeberg J Y. “Spectrophotometric image analysis of fine art paintings” Proceedings of IS&T and SID Fourth Colour Imaging Conference 1996; 50-53. [2] Farrel J E, Cupitt J, Saunders D, Wandell B A. “Estimating Spectral Reflectances of digital images of Art” Proceedings of International Simposium on Multispectral Imaging and Color Reproduction for Digital Archives 1999; 58-64. [3] Bruni S, Cariati F, Consolandi, L. Galli A, Guglielmi V, Ludwig N, Milazzo M. “Field and laboratory spectroscopic methods for the identification of pigments in a northern Italian eleventh century fresco cycle” Appl. Spec. 2002; 56(7): 827-833. [4] 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” App. Opt. 1998; 37(8):1299-1309. [5] Saunders D and Cupitt J. “Image processing at the National Gallery: The VASARI project” National Gallery Technical Bulletin 1993; 14: 72-85. [6] Cotte P and Dupouy M. Crisatel “High Resolution Multispectral System” Proceedings of PICS03 The Digital Photography Conference 2003;6:161-165 [7] Anglos D, Balas C, Fotakis C. “Laser spectroscopic and optical imaging techniques in chemical and structural diagnostic of painted artwork” American laboratory 1999; 31: 60-67. [8] Balas C. “An imaging method and apparatus for the non destructive analysis of paintings and monuments” International Patent App. PCT/GR00/00039 [9] CIE. Radiometric and photometric characteristics of materials and their measurement. CIE technical report 38. Wien: 1977.
Fig.1 Block diagram of the multispectral scanner
Fig.2 The collecting optics and the lighting system
Fig.3 The multispectral scanner
Fig.4 Spectral reflection factors measured with the developed device (colour circle) and certified by NPL in UK (black square) for a set coloured ceramic tiles
Fig.5 A few small scale digital images selected among the 32 monochrome images acquired with the developed multispectral scanner. The images depict a detail of a XII century of the wooden painting “Croce di Rosano”. The output of the shown channels corresponds to the following wavelengths: 420nm, 450nm, 500nm, 540nm, 580nm, 620nm, 740nm, 800nm.
Fig. 6 Colour image (1:1 scale with the original artwork) of a detail of the acquired area. The image was constructed starting from the 31 monochromatic images acquired in the 380-780 nm spectral region with the developed scanning spectrometer.
Fig.7 Croce di Rosano, a wooden cross dated XII century: (left) from top to bottom, RGB colour image, IR@800 nm image and false colour image, (right) spectral reflection factor of the area identified by a white square on the image. The black squares are an average over raw data on different adjacent points; the blue dots are the values measured by a contact spectrometer on a set of reference samples prepared at OPD laboratories.
Fig.8 Pisanello’s “Portrait of Lionello d’ Este: (from left to right) combinaton of multispectral colour image, IR reflectography, and false colour image.
Fig.9 Fresco dated between the VI and the VIII century, at “Santa Maria Antiqua” Church in Rome. Comparison between the RGB colour image (left) and a digital photograph (right) of the same detail representing the scene of a man’s torture.
∆R
ORANGE BLUE [%] [%]
CYAN YELLO [%] W [%]
RED [%]
GREEN [%]
PINK [%]
2.1
1.0
1.1
0.8
1.2
1.4
1.7
Table 1. Mean absolute deviation between measured and certified spectral reflection factor R.
Y
YREF
∆Y
x
xREF
∆x
y
yREF
∆y
ORANGE
41.6
38.8
2.8
0.48
0.49
0.01
0.39
0.38
0.01
BLUE
6.99
6.58
0.41
0.27
0.26
0.01
0.26
0.25
0.01
CYAN
24.3
24.2
0.10
0.22
0.22
0.00
0.26
0.26
0.00
YELLOW
64.3
64.6
0.3
0.42
0.43
0.01
0.46
0.46
0.00
RED
18.0
17.8
0.2
0.46
0.45
0.33
0.33
0.00
GREEN
27.1
26.6
0.50
0.28
0.28
0.00
0.39
0.39
0.00
PINK
17.7
17.1
0.6
0.37
0.37
0.00
0.31
0.31
0.00
0.01
Table 2. Comparison between measured and certified values of luminance and chromaticity coordinates.
