A Comparison of Small-Aperture and Image-Based Spectrophotometry of Paintings
Roy S. Berns, Lawrence A. Taplin, Francisco H. Imai, Ellen A. Day, and David C. Day
An experiment was performed that compared conventional small-aperture and image-based
reflection spectrophotometry of paintings. The imaging system used a liquid-crystal tunable filter,
resulting in 31 spectral bands evenly sampled between 400 and 700 nm and ranging in bandwidth
betweeti 10 and 60 nm. The small-aperture spectrophotometer had a constant bandwidth of 10 nm.
Test targets consisting of chromatic and neutral samples of various colors and spectral properties
were used to derive a calibration transformation between the two technologies. Three paintings
were analyzed: Saint Jerome Reading by Alvise Vivarini, Murnau by Alexej von fawlensky and Pot
of Geraniums by Henri Matisse, all from the collection of the National Gallery of Art, Washington,
DC. Average colorimetric accuracy varied between 2.0 and 3.2 ∆E00 units and the average spectral
accuracy varied between 1.0 and 2.1% spectral root-mean-square. Two drawbacks are that the
imaging system has a high uncertainty at short wavelengths, and the spectral matches for samples
with flat spectra are slightly worse than for other samples. Both limitations can be corrected by
changes in lighting, the calibration target, and the method of deriving the transformation matrix.
Nevertheless, the imaging system has the advantage of no moving parts and may not require image
registration, making it well suited to perform scientific imaging of cultural heritage. Furthermore,
the image-based spectra have sufficient accuracy for pigment identification and mapping.
INTRODUCTION
Visible region spectral reflectance measurements on paintings are a common analytical tool for art
conservation. This technology became available during the early twentieth century but was rarely
used for in-situ measurements because the measurement apertures were large, with a typical
diameter of about 4 cm, and not designed for potentially fragile objects. During the 1970s, Wassail
and Wright built a spectrophotometer for the measurement of paintings at the National Gallery,
London [1]. Although not movable, paintings could be positioned in front of the 5 mm circular
entrance aperture. Later in the decade, Wright developed a more portable version for the Courtauld
Institute of Art [2], reminiscent of older X-ray fluorescence instruments. During the early 1990s,
Bacci built a small-aperture fiber optics reflectance spectrophotometer, which he used at the Chiesa
del Carmine and Uffizi Gallery in Florence [3]. Similar commercially available instruments, for
example from Zeiss, made use of modular components such as the light source and spectrometer.
Also during the 1990s, small-aperture hand-held spectrophotometers were commercialized, mainly
for imaging-based industries. Berns, Krueger, and Swickhk used this instrumentation for pigment
selection for inpainting [4J. Due to their low cost and ease of use, these hand-held instruments are
today common tools, both for analytical work and for color specification using Commission
International de l'Eclairage (CIÉ) colorimetry.
One of the applications of the Wassail and Wright spectrophotometer was to track long-term color
changes in the National Gallery's collection. As digital imaging evolved during the 1980s, it became
apparent that an imaging approach could provide numerous advantages. A European initiative,
VASARJ, standing for Visual Arts System for Archiving and Retrieval of Images, was launched to
develop such an approach to recording the colorimetry of paintings at high resolution [5]. The
VASARI system recorded the reflectance properties between 400 and 700 nm using 7 channels,
each with a 70 nm bandwidth. Thus, the instrument was an abridged spectrophotometer. A
monochrome area array sensor of low resolution was scanned across the work of art.
Research followed along similar lines at the École Nationale Supérieure des Télécommunications
(ENST) in Paris [6, 7] and Consiglio Nazionale délie Ricerche (CNR) in Florence [8-10], During
2001, another European initiative was launched, CRISATEL*; a 13-channel high-resolution scanner
has been built, sampling the visible spectrum between approximately 400 to 800 nm in 40 nm
increments and between 750 and 1050 nm in 100 nm increments [11—13]. This scanner incorpor-
ates square-wave interference filters that do not overlap one another. The same filter set has also
been used with an area-array sensor [14].
This area of research is often referred to as multi-spectral imaging, 'multi' representing multiple
spectral bands. The term has its roots in the remote sensing field, but the authors prefer simply to
call this technique 'spectral imaging'. Approaches to spectral imaging continue to evolve, with the
accuracy of the colorimetric and spectral measurements dependent on the number of channels, their
bandwidths, and how the data are processed. Accuracy needs to be balanced against practical
considerations, such as the speed of acquisition, system complexity, expertise required for
successful operation, and cost.
The Munsell Color Science Laboratory (MCSL) has been active in spectral imaging and spectralbased color reproduction since the mid 1990s [15]. During 2001, a research program was initiated
to design and build spectral-based imaging systems for the National Gallery of Art, Washington,
DC and the Museum of Modern Art, New York [16]. One of the program goals was to evaluate
various approaches to spectral imaging. Three techniques are under evaluation. The first is most
similar to typical spectrophotometry [17-20]. A wavelength selection element is placed in front of a
monochrome digital camera. An image is captured at each wavelength in the visible spectrum,
typically every 10 nm, resulting m 31 or more image planes. This is a spectral measurement method
since a measurement is made at each wavelength of interest. The selection element could be a
diffraction grating, a set of interference filters, or a liquid-crystal tunable filter (LCTF); in research
at the MCSL a LCTF is used. The second technique is an abridged technique in which five or more
absorption or interference filters are placed in front of a monochrome digital camera: this is a
spectral estimation method [17, 18]. The third technique uses a color digital camera and
*Conservation Restoration Innovation Systems for image capture and digital Archiving to enhance
Training, Education and lifelong Learning
one or two absorption filters [20-27]. In this case, sets of color images are used to estimate spectral
data. For both estimation techniques, the filters are designed to maximize spectral accuracy.
Theoretically, the first technique achieves the best performance since it most closely replicates
traditional spectrophotometry. The third technique is expected to achieve the poorest performance
since it is constrained by its inherent design as a color camera rather than an imaging spectrometer.
However, the third technique has the advantages of simplicity and flexibility; since high-quality and
high-resolution professional-grade color cameras can be used to obtain both spectral and color
information.
Generally, performance is quantified using color targets svich as the GretagMacbeth ColorChecker
chart [28] or GretagMacbeth ColorChecker DC chart [29], and comparing image-based with
traditional-spectrophotometer-based spectral data. These targets have known spectral properties and
uniform surface characteristics and are used as de facto color standards in the color-imaging
community. This practice is analogous to evaluating the performance of a traditional
spectrophotometer [30, 31] using a set of color tiles, for example those certified by the British
Ceramic Research Association. However, this is a best-case scenario since the tiles (or color targets)
have uniform surface characteristics and are easy to measure, while paintings generally possess
neither of these properties. Therefore, it is critical to evaluate the spectral accuracy of the measurements on paintings in addition to those on color targets. Accordingly, an experiment was performed
in which three paintings were measured in situ using a small-aperture hand-held spectrophotometer
and imaged using a monochrome digital camera coupled with a LCTF. The experiment was
performed in the photography studio at the National Gallery of Art, Washington, DC, enabling
interaction with museum imaging professionals and a better understanding of the unique needs of
museums and other cultural heritage repositories when performing direct digital imaging of
paintings.
PAINTINGS
Three paintings were used in the experiment (Table 1). The main selection criteria were modest
size, a "wide range of colors including high chroma and low lightness, minimal impasto, and the use
of pigments with long-wavelength reflectance 'tails'. This last criterion is important because the red
sensitivity of many digital cameras is shifted to longer wavelengths compared with the human
visual system. An example of the effect that
Table 1 Paintings from the collection of the National Gallery of Art, Washington, DC, used in the
experiment
N = number of in-situ measurements
this can have is that it can cause ultramarine and cobalt blue to take on a purplish cast. The painting
by Vivarini was expected to reveal possible image noise and dynamic range limitations in dark
passages, inside the lion's cave for example. The paintings by Vivarini and Matisse contain blue
pigments with long-wavelength reflectance tails. Furthermore, the Matisse had been recently photographed conventionally and the colors of the pots could not be accurately reproduced. The surface
properties of the three paintings varied between matt and glossy.
IN-SITU MEASUREMENTS
A GretagMacbeth Eye-One hand-held spectrophoto-meter was used to measure the spectral
reflection properties of each painting. This spectrophotometer has a 4 mm circular aperture,
bidirectional geometry, and disperses light with a diffraction grating. It is lightweight and powered
by the host computer's USB interface; the authors have found it very well suited for in-situ
measurements. Transparent polymer film was used to fashion a template with a small hole and
cross-hairs. The template would both protect the paintings and locate the measurement. An attempt
was made to measure uniform color regions and the total number of spectral reflectance
measurements per painting depended on the number of uniform areas with unique color, listed in
Table 1. An Olympus 2000Z consumer digital color camera was mounted on a copy stand. Each
painting 'was placed on the copy stand and a digital image was taken of the template, following the
spectral measurement, to record the measurement position.
IMAGING SYSTEM, CALIBRATION AND VERIFICATION
Lighting
A pair of Elinchrom Scanlite Digital 1000 tungsten-halogen lights fitted with Chimera Softboxes
illuminated the object plane at 45° to the surface. The goal was to provide spatially uniform diffuse
illumination and minimize specular highlights; this geometry is often used when imaging paintings
for scientific analyses [5]. At the image plane, the lighting resulted in an illuminance of 1550 lux
and a correlated color temperature of 3334 K.
Camera
A Roper Scientific Photometries Quantix 6303E monochrome digital camera with a thermoelectrically cooled grade 3 Kodak 2048 x 3072 pixel CCD (model KAF-6303E) with readout speed
of 5 MHz was used. The sensor was coupled with a Cambridge Research Institute LCTF. By
electronically adjusting the retard-ance of the polarizing waveplates [7, 32], the peak wavelength of
the transmitted light can be selected in a precise and reproducible fashion, providing a rapid and
vibrationless tunable multi-filter system. This technique has the advantage of better maintaining
image registration for all the images captured compared with filter wheels. Depending on the
number of polarizers in the LCTF, the bandwidth of the filter can be varied. There is a tradeoff
between bandwidth and light throughput: decreasing bandwidth decreases the throughput.
Historically, opinions have differed over the 'optimal' sampling interval and bandwidth for
spectrophotometers designed for color technology. For example the Eye-One has a 10 nm
bandwidth, while many analytical spectrophotometers used for chemical analyses have bandwidths
between 2 and 5 nm. The first diode-array reflection spectrophotometers had a 20 nm sampling
increment and bandwidth. The use of circular interference filters as dispersing elements has resulted
in variable bandwidth. For most applications it is clear from statistical [22, 33] or frequency-based
[34, 35] analyses of reflecting colored materials that the traditional approach of an equal sampling
interval and a bandwidth between 2 and 10 nm is not required. Accordingly, the widest bandwidth
LCTF was used, maximizing light throughput. At this stage of the research, optimal sampling was
not addressed and data were simply collected at 10 nm increments, resulting in 31 channel images.
The spectral sensitivities of the camera and LCTF imaging system corresponding to wavelength
centroids from 420 to 700 nm in 20 nm increments are plotted in Figure 1. A property of this type
of dispersing element is the increase in bandwidth with increasing wavelength; the bandwidth
ranged from approximately 10 to 60 nm. At short wavelengths, sampling increment and bandwidth
were therefore matched, 'while at long wavelengths the spectrum was over-sampled since the
bandwidth was greater than the sampling interval. Because the LCTF has low transmittance and the
sensor has low sensitivity at short wavelengths, the sensitivity of the system in the blue region of
the visible spectrum is quite low compared with its sensitivity in the red region.
A Rodenstock 105 mm enlarger lens was coupled to the LCTF. The lens was set to an F-11 aperture
and focused once at 550 nm. All the images were thus of identical size and could be combined
'without the need for image registration. Since the sensor had low resolution and the object was to
investigate spectral and color accuracy within a 4 mm circular aperture, focusing was unnecessary.
For higher spatial quality, the camera should be refocused at each wavelength and image capture
followed by image registration as in the CRISATEL Jumboscan camera [11—13].
The camera has a 12-bit analog-to-digital converter corresponding to a digital range of 0-4095.
Optimal
exposure was determined based on imaging a "white Halon tablet; shutter speed was varied until the
average digital signal was 3800 +100. Images were stored as 16-bit TIFF files with linear
photometric encoding. A plot of exposure time versus wavelength is shown in Figure 2; the
exposure times are quite long at 400 and 410 nm. The exposure time at 400 nm was shorter than at
410 nm because of significant out-of-band transmittance at 400 nm due to hetero-chromatic stray
light. The total time to capture and record the 31 images was 500 seconds.
Calibration and verification targets
As with any optical device, calibration is required, and the goal was to develop a transformation that
converted raw image data to spectral reflectance factors. In essence, image-based measurements
were transformed to traditional contact reflectance measurements.
Two calibration targets were used; the first was the GretagMacbeth ColorChecker DC chart, which
has 237 samples reasonably distributed in color space. However, its range of pigmentation is
limited, particularly for blues and it appeared that the only blue pigment used was a phthalocyanine.
It was important that the calibration target had samples with long-wavelength reflection tails.
Accordingly, a separate target of 56 blues was produced using artists' acrylic paints including
ultramarine and
Figure 1 Spectral sensitivities of the imaging system. These spectra are a product of the sensor
sensitivity and LCTF transmittances.
Figure 2 Exposure time as a function of centroid wavelength.
cobalt-containing pigments. As a verification target, samples were made of typical artists' pigments
using Gamblin Conservation Colors. Each pigment was mixed with titanium white at two different
concentrations and applied to a canvas board. The GretagMacbeth ColorChecker chart was also
used as a verification target.
All the targets were measured using a GretagMacbeth XTH portable hand-held integrating-sphere
spectro-photometer with specular component excluded, approximating the lighting system geometry
as closely as possible. The instrument has a bandwidth of 10 nm and samples the spectrum between
360 and 750 nm in 10 nm increments. Only data between 400 and 700 nm were used for this
experiment.
A light gray poster board that was larger than the image area and which had a lightness (L*) of 70
was used to flat field the image plane digitally and to compensate for spatial variation in the spectral
response of the camera and the spectral power distribution of the illumination: both chromaticity
and luminance were included. Quite often white cards are used, but for these experiments this
approach would have been problematic. First, exposure was set using a Halon plaque positioned in
the center of the field of view. If this region was not the most highly illuminated region, clipping
might occur during flat fielding. Second, any 'hot' pixels might, in similar fashion, get clipped.
Finally, since flat fielding is a linear mathematical operation, a light gray card ensures that the
sensor is operating in its linear range of photometric response.
For each wavelength centroid, images were collected of the light gray card, the various color
targets, each painting and, finally, with the shutter closed. This last image is used to account for the
dark current from the detector and is analogous to measuring a black trap in conventional
reflectance spectrophotometry.
Calibration transformation
Each image plane was first corrected for fixed pattern noise (by subtraction of the dark image) and
digitally flat fielded (by dividing by a spatially uniform target of known reflectance factor, in this
case the light gray poster board). The flat fielding compensated for lack of lighting uniformity,
differences in LCTF transmittance as a function of location across the filter and as a function of
wavelength, and variability in spectral sensitivity across the sensor. Digital compensation is critical
when using LCTF technology.
Each pixel corresponding to the calibration color targets was assigned a measured spectral
reflectance factor. Using a generalized pseudo-inverse calculation that incorporated singular-value
decomposition [36], a 31 x 31 matrix transformation was derived from 230640 pixels. This is
analogous to fitting a line to X and Y scatter data using linear regression. Using the individual pixels
rather than their average for each color sample improved spectral accuracy significantly because
noise properties of the imaging system were factored into the mathematical transformation. This
pixel-based method consisted of masking regions of the images corresponding to the uniform colors
and using all the camera signals inside the masked region to build the transformation.
A visualization of the matrix transformation is shown in Figure 3. This matrix accounts for
differences in bandwidth between the LCTF and spectrophotometer, wavelength calibration,
unwanted transmittance in out-of-band regions such as at 400 nm, and the narrow count range of
digital signals for the Halon target. The transformation has the expected diagonal 'mountain range';
each reflectance factor wavelength was largely determined from the camera image with the same
wavelength centroid. Because the LCTF has significant out-of-band transmittance at 400 nm, there
is a 'valley' along the left edge. The negative coefficients adjacent to the matrix diagonal
compensate for the changing bandwidth; as the centroid wavelength increases, the coefficients
increase in negativity. This is an approximation to compensating for uneven bandwidth by convolution [37]. Visualizing the transformation has proved to be very useful to determine whether the
imaging system is performing correctly. For example, if excessive stray light is captured, perhaps
by insufficient baffling of the
Figure 3 Visualization of the mathematical transformation that converts image data (0-4095) to
spectral reflectance factor (0-1).
lighting system, the transformation may appear as a mountainous diagonal surrounded by
mountains at a lower elevation [38].
The efficacy of this calibration method is shown in Figure 4. The average spectral difference
between the spectral camera and the small-aperture spectrophoto-meter was nearly zero at all
'wavelengths for the ColorChecker DC chart and very small for the blue acrylic target. Greater
uncertainty was expected for the blue target since it was hand painted and had much poorer spatial
uniformity than the ColorChecker DC chart, which is produced using a film spreader. At every
wavelength, a scatter plot could be made comparing the two instruments and a line fitted to these
data; a correlation coefficient for the line fit indicates the amount of scatter. To produce a number
that increases with the magnitude of the spectral differences, the correlation coefficient, ranging
between zero and unity, was subtracted from unity; perfect correlation would yield zero. These
values are plotted in Figure 4 as a function of wavelength. For both targets, correlation worsened at
short wavelengths because the imaging system had its poorest signal-to-noise properties in this
wavelength region: the detector had low quantum efficiency, the LCTF had low transmittance, and
the light source had low radiance.
The spectral data generated by conventional spectro-photometry and imaging were analyzed for
spectral accuracy using spectral reflectance root-mean-square
(RMS) difference and an index of metamerism between illuminants D65 and A, and for colorimetric
accuracy using the CIE color difference equation CIEDE2000 for D65. The 2° observer was used for
all calculations and these data are summarized in Table 2. The metameric index incorporated a
correction such that each pair of spectra had zero color difference for a reference illuminant [39]. A
CIEDE2000 color difference was then calculated for a test illuminant very different to the reference
illuminant. The poorer the spectral match, the greater the metameric index. This spectral metric is
useful because it is defined in color difference units. A numerical example is given in Reference 30.
The metameric index was calculated with D65 as the reference illuminant and illuminant A as the
test illuminant, and vice versa. The former index penalizes lack of fit at long wavelengths, often a
problem using imaging techniques, while the latter metric penalizes short wavelengths. The average
color difference for the ColorChecker DC chart was very small, verifying the efficacy of the
calibration technique. For the blue acrylic target, differences increased for the reasons described
above. It was observed that small differences in spectral fit often translated into appreciable
differences in colorimetry. Deriving meaningful metrics for spectral imaging is a current topic of
research [40, 41].
The GretagMacbeth ColorChecker chart has become a de facto color-imaging standard because of
its excellent design features including materials with near-infrared
Figure4 Average spectral difference, Rimage,λ ,- Rsmall_aperture,λ (solid line) and one minus the
correlation coefficient (dashed line) for the ColorChecker DC and blue acrylic calibration targets.
Table 2 Average performance metrics comparing conventional small-aperture in-situ spectrophotometry and spectral imaging for the various calibration (ColorChecker DC, blue acrylics) and
verification (Gamblin Conservation Colors, ColorChecker) targets.
SD = standard deviation.
reflectance tails, high chroma colors, and a gray scale [28]. Accordingly, the spectral difference
analyses using small-aperture and imaging-based spectrophotometry are plotted in Figure 5.
Although not plotted, the spectra for the chromatic samples are similar, corresponding well to the
absorption properties of each sample. This spectral similarity is sufficiently accurate for pigment
identification [4, 42] and mapping applications [9, 10]. The performance statistics listed in Table 2
are excellent, but there were several systematic trends in the spectra. For neutral samples and
samples with regions of constant spectral reflectance, the spectral matches were slightly worse than
for the other samples. The mean difference as a function of wavelength was less smooth than the
ColorChecker DC chart. This was caused by the method of transformation depicted in Figure 3. The
matrix was not constrained for smoothness, so that each wavelength was independent and any
smoothing was a function of the spectral properties of the calibration targets. The majority of the
calibration target samples on the ColorChecker DC and blue pigment charts were highly chromatic,
resulting in sharp transitions in reflectance factor. These samples are well suited to correct for
wavelength and bandwidth differences of the LCTF compared with the conventional spectrophotometer. However, the preponderance of chromatic
samples resulted in a lack of smoothness for samples with regions of constant spectral reflectance.
The second systematic trend was the reduction in correlation at short wavelengths.
The Gamblin Conservation Colors target was also an important target to analyze since it contains
common artists' pigments. Furthermore, because each colorant was mixed with white, the spectral
range was maximized, making this a difficult target to match. The spectral difference analyses are
plotted in Figure 5 and the performance statistics are listed in Table 2. Given that this target was
also hand painted, the results were excellent.
In-situ analyses
Based on the specified measurement aperture of the spectrophotometer and images of the
measurement locations, an image mask was made for each painting. The spectral reflectances of
pixels within the mask were averaged and compared with the small-aperture measurements. In some
cases, the image-based spectral data were quite different from the direct measurements. One of the
difficulties during the in-situ measurements was keeping the polyester template in position
following the removal of the spectrophotometer. It is suspected that
Figure 5 Average spectral difference, Rimage,λ - Rsmall_aperture, λ (solid line) and one minus the
correlation coefficient (dashed line) for the GretagMacbeth ColorChecker and Gamblin
Conservation Colors targets.
the Olympus camera images did not accurately record the measurement positions in these cases. In
local regions near each position, spectral RMS differences between the direct measurements and
each pixel were calculated. If the position were incorrect a large spectral difference would result.
This is shown in Figure 6 for measurement position 19 on Pot of Geraniums. For each
measurement, the aperture was moved by up to a maximum of 40 pixels until spectral RMS
difference was minimized. As seen in Figure 6, there can be a range of reflectances for a visually
uniform region, caused by brush strokes and craquelure.
The spectral and colorimetric accuracy for all the paintings are listed in Table 3. Spectral analysis
plots are shown in Figure 7. For Jawlensky's Mumau, the similarity between the camera and smallaperture measurements was very good, with a quality of performance similar to the Gamblin
Conservation Colors verification target. Good performance was expected for this painting as the
spectra were the least complex and the range of colors was small. Many of the colors were dark,
resulting in limited spectral variability. Much of the painting was matt, since it is unvarnished.
Consequently, differences in geometry between the in-situ spectrophotometer (the bidirectional
Eye-One), the spectrophotometer used on the calibration targets (the integrating sphere Macbeth
XTH) and the camera system had a small effect. For Vivarini's Saint Jerome Reading, the spectral
dissimilarity was slightly greater than for Mumau, although still within
Figure 6 Spectral RMS difference map comparing each pixel's RMS difference with the measured
value using a small-aperture spectrophotometer for position 19 of Pot of Geraniums. The dotted
circle locates the assumed measurement position.
the level of the verification test targets. However, because the colors were dark, the average color
differences were greater than those achieved for the test targets. Small differences in spectral
reflectance translated into appreciable colorimetric differences. There was also a systematic trend
where the image-based spectra were under-predicted, a result of the varnished painting having a
moderately glossy surface. Matisse's Pot of
Table 3 Comparisons between spectrophotometry and imaging for three paintings.
SD = standard deviation.
Figure 7 Average spectral difference, Rimage,λ,- Rsmall_aperature, λ (solid line) and one minus the
correlation coefficient (dashed line) for the three paintings measured.
Geraniums exhibited the greatest differences between small-aperture and image-based spectral
measurements. This painting is thinly varnished, yielding a semi-glossy surface. Semi-glossy
materials are the most sensitive to differences in measurement geometry [30], There was
considerable variability between 500 and 600 nm, based on the correlation plot in Figure 7. Many of
the colors measured had reflectances changing from low to high reflectance and vice versa in this
wavelength range. Several examples are plotted in Figure 8 and the 43 measurement positions for
Pot of Geraniums and their small-aperture and image-based spectra are shown in Figure 9.
Overall, the image-based spectra were excellent spectral 'fingerprints' and could be used as an
analytical tool for conservation science, in a similar fashion to other spectral measurements such as
Raman and X-ray fluorescence. The authors have also already shown how spectral images can be
used for pigment selection for inpainting using spectra with lower accuracy than described in this
experiment [42].
CONCLUSIONS
Image-based spectrophotometry was compared with traditional, small-aperture spectrophotometry
by
Figure 8 Spectral reflectance factor measurements for four samples of Matisse's Pot of Geraniums
using small-aperture (solid lines) and imaging-based (dashed lines) spectrophotometry.
analyzing three paintings that have a range of coloration, spectral properties, and surface attributes.
The correlation between the two methods of spectral measurement was reasonable, resulting in
average colorimetric differences between 2.0 and 3.2 AE„„ color difference units and average
spectral RMS differences between 1.0 and 2.1%. There were systematic differences caused by the
poor signal-to-noise properties of the imaging system at short wavelengths and interrelationships
between measurement geometry and surface attributes.
There are several opportunities for improvement. First, the calibration targets had a very small
range of reflectances at the shortest wavelengths, caused by the use of titanium dioxide white. It
would be interesting to replace the titanium white with a different scattering pigment that could
yield higher reflectance at shorter wavelengths. This would improve accuracy for short wavelengths
when imaging paintings containing lead white.
The poor signal-to-noise properties at short wavelengths were caused by the detector, the LCTF and
the light source having low sensitivity, transmitían ce and radiance, respectively. An obvious
remedy would be to replace the tungsten-halogen lights by a source with greater short-wavelength
radiance such as a Xenon lamp.
The transformation matrix treated each wavelength independently. As a consequence, spectra with
regions of constant reflectance factor, such as neutrals, had excessive spectral variability. Adding a
smoothness constraint to the matrix could improve performance. An alternative approach would be
to add a weighting function to each sample comprising the calibration target; weighting the neutral
samples more heavily would improve smoothness. This could also provide opportunities for
improved performance for specific colorants. The transformation could be optimized to achieve best
estimation accuracy for certain colors. The targets used in this experiment were not designed for
direct imaging of cultural heritage. It seems likely that improvements in target design will improve
spectral estimation accuracy; this is an active area of research for the authors [33, 43].
The transformation matrix was optimized to minimize spectral RMS error. This does not lead to
minimum color difference. Further optimization could be performed to improve colorimetric
accuracy for a specific illuminant and observer, an approach used by the authors when imaging with
a RGB digital camera [25-27].
Although the system can be improved, it is nonetheless well suited as an analytical spectral
instrument. By adding a computer-controlled lens-focusing system, image acquisition can be fully
automated. Since the spectral sampling is computer controlled, there are many opportunities to
improve the data by having uneven spectral sampling, reducing the number of channels, and adding
wide-band acquisition by temporal processing in addition to the usual spectral processing.
ACKNOWLEDGMENTS
The authors would like to thank the National Gallery of Art, Washington, DC, the Museum of
Modern Art, New York, and the Andrew W. Mellon Foundation for their financial support of the
Art Spectral Imaging (Art-Si) Project. We also acknowledge the assistance of the Division of
Imaging and Photographic Services and the Division of Conservation at the National Gallery of Art.
SUPPLIERS
British Ceramic Research Association Series II tiles: CERAM Research, Queens Road, Penkhull,
Stoke-on-Trent, ST4 7LQ, UK.
Figure 9 Measurement positions on Matisse's Pot of Geraniums along with their corresponding
spectral reflectance factor measurements using small-aperture (red lines) and imaging-based (blue
lines) spectrophotometry (measurement position indexing is from left to right by row).
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AUTHORS
ROY S. BERNS is the Richard S. Hunter Professor in color science, appearance, and technology at
the Munsell Color Science Laboratory and graduate coordinator of the color science graduate
programs within the Center for Imaging Science at Rochester Institute of Technology. He received
both BSc and MSc degrees in textile science from the University of
California at Davis and a doctoral degree in chemistry with an emphasis in color science from
Rensselaer Polytechnic Institute. He is currently directing a joint research program in museum
imaging with the National Gallery of Art, Washington, DC and the Museum of Modern Art, New
York. He is also collaborating with the Art Institute of Chicago and the Van Gogh Museum in
digitally rejuvenating paintings that have undergone undesirable color changes. Address: Munsell
Color Science Laboratory, Chester F. Carlson Center fir Imaging Science, Rochester Institute of
Technology, 54 Lomb Memorial Drive, Rochester, NY Í4623-5604, USA. Email: bems@cis. rit.edu.
LAWRENCE A. TAPLIN is a color scientist with the Munsell Color Science Laboratory at the
Rochester Institute of Technology where he received a MSc in color science. He also holds a BSc in
computer science from the University of Delaware. His research is focused on spectral imaging of
museum artwork for digital archiving and reproduction. Address as for Berns. Email:
taplin@cis.rit. edu
FRANCISCO HIDEKI IMAI received his BSc in electronical engineering and MSc in electronics and
computer engineering from Technological Institute of Aeronautics (ITA — Brazil) and a doctorate
in imaging science from Chiba University in Japan. From 1997 to 2003 he worked at the Munsell
Color Science Laboratory at Rochester Institute of Technology, first as postdoctoral fellow and later
as senior color scientist. Since October 2003 he has been working as a senior color scientist at
Pixim in Mountain View, California. Address: Pixim, Inc., 1395 Charleston Road, Mountain View,
CA 94043, USA. Email: francisco@pixim. com
ELLEN A. DAY has a BSc in imaging and photographic technology and a MSc in color science, both
from Rochester Institute of Technology. She is currently a color scientist at Pantone in Carlstadt,
New Jersey. Address: Pantone, Inc., 590 Commerce Boulevard, Carlstadt, NY 07072-3098, USA.
Email: eday@pantone.com
DAVID COLLIN DAY received both BSc and MSc degrees in imaging science from Rochester
Institute of Technology. He is currently employed as an imaging engineer working for the HewlettPackard Company in Boise, Idaho. Address: Hewlett-Packard Co., Ii3ti West Chinden Boulevard,
Boise, ID 83714, USA. Email: david.c.day@ hp.com
Résumé — On a mené une expérience pour comparer la spectrophotométrie de réflexion à petite
ouverture conventionnelle à celle basée sur l'imagerie pour l'examen des peintures. Le système
d'imagerie comprenait un filtre à accord variable à cristaux liquides produisant 31 bandes
spectrales régulièrement échantillonnées entre 400 et 100 nm et ayant une largeur de bande variant
entre 10 et 60 nm. Le spectrophotomètre à petite ouverture avait une largeur de bande constante de
10 nm. On a utilisé des cibles-tests consistant en des échantillons chromatiques et neutres de
couleurs variées et les propriétés spectrales ont servi à établir une transformation de calibration
entre les deux technologies. Trois peintures ont été analysées : Saint Jérôme lisant, d'Alvise Vivarni,
Murnau, d'Alexej vonjawlensky, et Pot de géraniums, d'Henri Matisse, toutes provenant de la
collection de la National Gallery of Art de Washington D.C. La précision colorimétrique moyenne
variait entre 2,0 et 3,2 unités de ∆E00 et la précision spectrale moyenne entre 1,0 et 2,1 % de
moyenne quadratique spectrale. Le système d'imagerie possède deux inconvénients : il y a un haut
degré d'incertitude aux courtes longueurs d'onde, et l'accord spectral est légèrement moins bon
dans le cas des échantillons ayant un spectre plat. Ces deux restrictions peuvent être corrigées par
des modifications de l'éclairage, de cible de calibration et de méthode de dérivation de la matrice
de transformation. Néanmoins, le système d'imagerie possède l'avantage de ne pas comporter de
parties mobiles et peut ne pas nécessiter de superposition d'images, ce qui en fait un outil bien
adapté pour l'imagerie scientifique dans le domaine du patrimoine culturel. En outre, les spectres
obtenus par cette méthode sont suffisamment précis pour identifier et cartographier les pigments.
Zusammenfassung — Zum Vergleich eines konventionellen niederapperturigen und eines
bildbasierten Spektrophotometers wurde an Gemälden Experimente durchgeführt. Das
Abbildungssystem nutzte einen einstellbaren Flüssigkristallfilter, mit Hilfe dessen zwischen 400 und
lOOnm 31 spektrale Banden mir Bandweiten zwischen 10 and 60 nm aufgenommen werden
konnten. Das niederapperturige Spectrophotometer hatte eine konstante Bandweite von lOnm. Die
Testobjekte bestanden aus chromatischen und neutralen Proben verschiedener Farben und
spektraler Eigenschaften. Sie wurden zur Ableitung einer Übertragungsmöglichkeit der Kalibration
zwischen den beiden Technologien genutzt. Drei Gemälde wurden untersucht: Der Lesende
Hieronimus von Alvise Vivarini, Murnau von Alexej von fawlensky und Schale mit Geranien von
Henri Matisse, alle aus der Sammlung der National Gallery of Art, Washington DC. Die
durchschnittliche kolorimetrische Genauigkeit lag zwischen 2.0 und 3.2 ∆E00 Einheiten und
durchschnittliche spektrale Genauigkeit zwischen 1.0 und 2.1% spektraler Effektivwert. Die
Nachteile Bildsystems liegen in der hohen Ungenauigkeit bei kurzen Wellenlängen und darin, daß
bei flachen Spektralkurven die Übereinstimmung der Spektren schlechter als üblich ist. Beide
Beschränkungen können durch Veränderung der Beleuchtung, der Kaibrationsobjekte und der
Methode der Ableitung der Transformationsmatrix korrigiert werden. Indessen, hat das Bildsystem
den Vorteil, daß keine beweglichen Teile vorhanden sind, weshalb keine Bilderfassung notwendig
ist. Dies macht es gut geeignet für wissenschaftliche Untersuchungen an unserem Kulturerbe.
Darüber hinaus haben bildbasierte Spektren eine hinreichende Genauigkeit, für
Pigmentbestimmung und -kartierung.
Resumen — En la presente investigación se ideó un experimento que comparaba, en pinturas
reales, espectrofotometría convencional de baja apertura con espectrofotometría de reflexión
basada en la imagen. El sistema de imagen usaba un filtro de cristal líquido regulable,
obteniéndose así 31 bandas espectrales muestreadas uniformemente entre 400 y 700 nm, en un
rango de ancho de banda entre 10 y 60 nm. El espectrofotómetro de pequeña apertura tenía un
ancho de banda constante de 10 nm. Los test de prueba, que consistían en muestras cromáticas y
neutras, de varios colores y propiedades espectrales, fueron usados para derivar una
transformación en la calibración entre las dos tecnologías. Se analizaron tres pinturas: San
Jerónimo leyendo por Alvise Vivarini, Murnau por Alexei von fawlensky y Vaso con geranios por
Henri Matisse, todas ellas de la colección de la National Gallery of Art, Washington DC. El valor
medio de precisión calorimétrica variaba entre 2.0 y 3.2 ∆E00 unidades y el valor medio de
precisión espectral (en raíz cuadrada) variaba entre el 1.0 y el 2.1%. Hay dos inconvenientes
debido a que el sistema de imagen tiene una alta incertidumbre en los resultados de cortas
longitudes de onda, y que las combinaciones de los espectros para muestras con espectro plano son
peores que con otras muestras. Ambas limitaciones pueden ser corregidas mediante cambios en la
iluminación, en el objetivo de calibración y con el método de derivación de la matriz de
transformación. De cualquier manera, el sistema de imagen tiene las ventajas de no presentar
partes móviles y de que no requiere procesos de registro de imagen, haciéndolo perfectamente apto
para estudio científico por imagen del patrimonio cultural. Aún es más interesante el hecho de que
los espectros basados en la imagen tienen suficiente precisión para utilizarlos en la identificación
de pigmentos y obtenciones de "mapas".