Practical Spectral Imaging Using a Color-Filter Array Digital Camera Roy S. Berns and Lawrence A.
Executive Summary Imaging is an important technique in the scientific examination of art. Its main use has been for visual documentation. Photographs have long been used to document condition before and after transit, microscopic examinations, conservation treatments, and so on. They are used to enable color reproductions in books and from the Internet.
Spectral Imaging System Specification The system uses commercial software and hardware to the greatest extent possible. The camera system revolves around Sinar AG equipment. The computer is an Apple platform. The printer is an Epson medium or large-format inkjet printer. The camera system is shown in Figure 1. A cost summary is given in Table I. The specific components are listed in Tables II –XI. These were the current costs during April 2005. Figure 1. Modified Sinar camera system. Table I.
Table II. Camera, shutter, and lens costs.
Table VI. Computer costs. Computer Apple Dual 2.5 Ghz G5 8GB/500GB/23" LCD IBM T221-DG5 22.2 inch QUXGA-W Manufacturer Part Number Apple 9503DG5 IBM Price Qty Ext Price $8,723 1 $8,723 $8,399 1 Total $8,399 $17,122 Price Qty Ext Price $13,848 $3,399 1 1 Total $13,848 $3,399 $17,247 Price $1,900 $900 $900 $800 $800 $599 $0 Qty 1 1 1 1 1 1 1 Ext Price $1,900 $900 $900 $800 $800 $599 $0 $2,995 1 Total $2,995 $8,894 Table VII. File server costs. File Server Apple XRaid 5.
Table IX. Color calibration costs.
Practical Spectral Imaging Using a Color-Filter Array Digital Camera Roy S. Berns Lawrence A. Taplin Mahdi Nezamabadi Yonghui Zhao Mahnaz Mohammadi Munsell Color Science Laboratory Chester F.
tested using colored targets and an oil painting, Pot of Geraniums by Henri Matisse. Comparisons were made with an imaging spectrometer consisting of a monochrome sensor and a liquid-crystal tunable filter and a commercial RGB digital camera. The new, practical system had the highest colorimetric accuracy of the three systems and equivalent spectral accuracy to the 31band imaging spectrometer.
could also use an RGB camera with and without a single colored filter [4].) By imaging a calibration target with a number of colored patches (e.g., GretagMacbeth ColorChecker DC or SG), a transformation is derived that converts the six signals to spectral reflectance factor. In our research, spectral reflectance factor ranges between 380 and 730 nm in 10 nm increments with a constant 10 nm bandwidth.
pixels that were adjacent to either red or to blue pixels. For the three-plane image, only the red, blue, and red-adjacent green pixels were used. The physical movement of the two filters resulted in a registration difference between the pair of three-plane images, typically on the order of ten pixels (about 0.25%).
CCD sensor, filters had to be defined and fabricated. An optimization was performed to select filters from among the Schott filter glass catalog. The optimization considered spectral and colorimetric accuracy, image noise, capture time, and fabrication simplicity and cost [9 – 11]. There were a number of different filter pairs with similar performance. We selected a pair in which one of the filters resulted in spectral sensitivities similar to the sensor with its original blue-green cover glass.
based on the Wyszecki hypothesis that a stimulus can be decomposed into a fundamental stimulus and a metameric black [13]. Our approach was similar to the Fairman technique of transforming a parameric pair into a metameric pair [14]. Conceptually, the fundamental stimulus corresponds to the spectral information that our visual system processes. The metameric black corresponds to the spectral information that is not processed; hence it has no color and it is black.
computed from S, a [n × 1] vector of the spectral power distribution of the reference light source, y , a [n × 1] vector of the reference luminance color matching function, and xyz , a [n × 3] matrix of the reference color matching functions. Matrix T!E is a [3 × (i+1)] transformation matrix 00 from digital counts to tristimulus values fit using nonlinear optimization described below.
integral shutter. This system was used routinely at NGA. The calibration target was a GretagMacbeth ColorChecker SG. Commercial software was used to create an ICC camera profile for the NGA system. The MCSL-Sinar and NGA-Sinar systems used the identical lighting set-up, a pair of Broncolor Xenon strobes, positioned at approximately a 70° angle from the surface normal on either side of the artwork. One side had about twice the irradiance. This resulted in a quite collimated, raking illumination.
research is still required to develop an improved calibration target for imaging paintings. Guiding principles should include those defined for exemplifying color-order systems [20]. The color management at NGA was very good. The listed results are typical of a well color-managed area-array color sensor camera system [21, 22]. The modifications to the Sinar camera resulted in marked improvement, particularly for the Gamblin target that consisted of typical artist pigments used in paintings.
units. This index is a ∆E00 value for CIE Illuminant A following a slight spectral adjustment [14] such that for CIE Illuminant D65, the colorimetric data are identical, that is, a ∆E00 of zero. [The spectral adjustment is the Fairman parameric correction, shown in Eq. (4).] The spectral rootmean-square (RMS) error over the wavelength range of 380-730 nm was also calculated. The Quantix-LCTF system had higher spectral accuracy than the MCSL-Sinar system.
unity. Perfect correlation would yield zero. These values as a function of wavelength are plotted in Figure 6 as the dashed red line. For both systems, large scatter occurred for short wavelengths. This was caused largely by the calibration targets containing titanium dioxide white. It has very low reflectance in this wavelength range and the spectral variation of the target patches was very small. This resulted in large uncertainty when estimating these wavelengths using each camera system.
sources (nominal CCT of 5000 K) and the image rendered on a color-managed CRT display [3]. Their digital master image files included these visual adjustments. The colorimetric accuracy of Pot of Geraniums is summarized in Table 5. The QuantixLCTF system had the best colorimetric performance on average, likely a result of using diffuse rather than directional illumination. This underscores how lighting for aesthetic purposes may not result in optimal images for scientific purposes.
illumination geometry, the calibration target spectrophotometric measurement geometry, and differences in gloss between the calibration targets and the painting. An interesting result was that the correlation spectra were similar in shape for both systems. The spectral performances were not statistically significantly different. Conclusions A practical spectral-based imaging system has been developed in which a color filter array (CFA) digital camera was combined with two absorption filters.
Acknowledgements The authors would like to thank the National Gallery of Art, Washington, D.C., the Museum of Modern Art, New York, the Andrew W. Mellon Foundation, and the Institute of Museum and Library Services 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. References 1 Berns, R.S.
10 Zhao, Y., Taplin, L.A., Nezamabadi, M., and Berns, R.S., ‘Methods of Spectral Reflectance Reconstruction for A Sinarback 54 Digital Camera’, MCSL Technical Report, http://www.cis.rit.edu/mcsl/research (2004). 11 Berns, R.S., Taplin, L.A., Nezamabadi, M., Mohammadi, M., ‘Spectral imaging using a commercial color-filter array digital camera’, in ICOM Committee for Conservation 14th Triennial Meeting, The Hague, 12- 16 September 2005, Netherlands (2005) 743- 750. 12 Zhao, Y., Taplin, L.A., Nezamabadi, M.
23 Zhao, Y., Berns, R.S., Okumura, Y., and Taplin, L.A., ‘Improvement of spectral imaging by pigment mapping’, in IS&T/SID Thirteenth Color Imaging Conference, 7- 11 November 2005, Scottsdale (2005) 40- 45. 24 Mohammadi, M. and Berns, R.S., ‘Diagnosing and correcting systematic errors in spectralbased digital imaging’, in IS&T/SID Thirteenth Color Imaging Conference, 7- 11 November 2005, Scottsdale (2005) 25- 30. Tables Table 1. Filter specifications for the optimized filters. Description Layer 1 (1.
Table 2. Test targets used for calibration and verification. Target Abbreviation GretagMacbeth ColorChecker ColorChecker or Color Rendition Chart CC GretagMacbeth ColorChecker DC ColorChecker DC or (Digital Camera) CCDC Number of Samples 24 240* *The central white square was treated as four samples.
Table 3. Colorimetric performance summary for the three camera systems and a best-case computation where the same target was used for both calibration and verification. Matched ColorChecker DC ColorChecker DC Calibration and and Blues and Blues ColorChecker Verification Calibration Calibration SG Calibration Quantix-LCTF MCSL-Sinar NGA-Sinar Best Case MCSL-Sinar ∆E00 ∆E*ab ∆E00 ∆E*ab ∆E00 ∆E*ab ∆E00 ∆E*ab ColorChecker DC Average 0.7 1.2 1.0 1.5 0.8 1.2 2.7 4.2 Maximum 5.
Table 4. Spectral performance for the two RIT systems and a best-case computation where the same target was used for both calibration and verification. Matched Calibration ColorChecker DC and ColorChecker DC and and Verification Blues Calibration Blues Calibration Quantix-LCTF MCSL-Sinar Best Case MCSL-Sinar Metameric Metameric Metameric Index Spectral Index Spectral Index Spectral (D65A) RMS (D65A) RMS (D65A) RMS (∆E00) (%) (∆E00) (%) (∆E00) (%) ColorChecker DC Average 0.
Table 5. Colorimetric performance summary for the three camera systems for Pot of Geraniums. ColorChecker DC Gamblin and Blues Conservation Calibration Colors Calibration Quantix-LCTF MCSL-Sinar NGA-Sinar ICC NGA-Sinar Digital Digital Master Digital Master Color Managed Master ∆E00 ∆E*ab ∆E00 ∆E*ab ∆E00 ∆E*ab ∆E00 ∆E*ab Average 2.3 3.3 2.7 3.7 3.5 4.8 5.8 7.4 Maximum 9.6 12.4 11.7 14.3 12.2 14.2 13.0 16.1 Std. Dev. 1.9 2.4 2.2 2.8 2.2 2.9 2.1 3.
Figures Figure 1. RIT’s digital workflow. Areas in green are camera software processing. Areas in pink are RIT software processing. Figure 2. Spectral transmittance of optimized filters described in Table 1.
Figure 3. Absolute quantum efficiency (spectral sensitivity) of the Kodak KAF 22000CE color filter array (data provided by the Eastman Kodak Company).
Figure 4. Normalized (to peak height) spectral sensitivities of the Kodak KAF 22000CE color filter array with each optimized filter in the optical path. Solid lines represent the blue-green filter sandwich and the dashed lines represent the yellow filter sandwich. (The detector cover glass and lens spectral transmittances are not included.) Figure 5.
Figure 6. GretagMacbeth ColorChecker average spectral difference (solid line), Rimage,λ – Rsmall_aperture,λ, and one minus the correlation coefficient (dashed line) for the Quantix-LCTF (top) and MCSL-Sinar (bottom) systems. .
Figure 7. Spectral comparison between reference spectrophotometer (red) and imaging system (blue). Top: Quantix-LCTF; bottom: MCSL-Sinar.
Figure 8. Pot of Geranium’s average spectral difference (solid line), Rimage,λ – Rsmall_aperture,λ, and one minus the correlation coefficient (dashed line) for the Quantix-LCTF (top) and MCSL-Sinar (bottom) systems.
Other Figure, may or may not be used Figure 27. Average ∆E00 colorimetric error for each listed imaging system. Blue: the MCSL-Sinar system; red: the Quantix-LCTF system; yellow: the color-managed NGA-Sinar system; green: the digital master (following visual editing) NGA-Sinar system.
Figure 7. The average spectral root-mean-square performance of each imaging system for each listed target.
Figure 10. Spectral comparison between reference spectrophotometer (red) and imaging system (blue). Top: MCSL-Sinar; Bottom: Quantix-LCTF.
Figure 5. The average colorimetric performance of each imaging system for each listed target.
Figure 1. NGA’s digital workflow. Areas in green are camera software processing. Areas in red are Adobe Photoshop software processing.
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An optimization routine is run at the command line (left), and calls M-files defining the objective function (top right) and constraint equations (bottom right). Defining, Solving, and Assessing Optimization Problems The Optimization Toolbox includes the most widely used methods for performing minimization and maximization. The toolbox implements both standard and large-scale algorithms, enabling you to solve problems by exploiting their sparsity or structure.
A user-defined output function (top) plots the current iterate at each algorithm iteration (left). The Optimization Toolbox also provides details for each iteration (bottom). Multi-Objective Optimization Multi-objective optimization is concerned with the minimization of multiple objective functions that are subject to a set of constraints. The Optimization Toolbox provides functions for solving two formulations of multi-objective optimization problems: goal attainment and minimax.
Quadratic and Linear Programming Quadratic Programming Quadratic programming problems involve minimizing a multivariate quadratic function subject to linear equality and inequality constraints. The toolbox implements three methods for solving these problems: trustregion, preconditioned conjugate gradient, and active set. Trust-region is used for bound constrained problems. Preconditioned conjugate gradient is used for problems subject to equality constraints.
Signal Processing Toolbox 6 Perform signal processing, analysis, and algorithm development The Signal Processing Toolbox is a collection of industry-standard algorithms for analog and digital signal processing. It provides graphical user interfaces (GUIs) for interactive design and analysis and command-line functions for advanced algorithm development. Most toolbox functions are implemented in the open MATLAB® language.
A spectrogram of the input signal showing the signal’s time-frequency distribution and power spectral density, created using the Signal Processing Toolbox spectrogram demo. Signal and Linear System Models Transforms The Signal Processing Toolbox provides a broad range of models for representing signals and linear time-invariant systems, including representations for transfer functions, state space, and zero-pole gain.
FDATool provides access to most FIR and IIR filter design methods in the toolbox. Additionally, you can: • Design filters by graphically placing poles and zeros in the z-plane • Analyze filters by examining magnitude, phase, impulse, and step responses, group delay, and pole-zero plots Magnitude response of the specified digital filter, created using FVTool. Data markers determine the frequency and magnitude values of any chosen data point.
The Window Viewer in WINTool displays the time-domain and frequency-domain representations of multiple windows for easy comparison. WINTool lets you design and analyze spectral windows. You can: Required Products Platform and System Requirements MATLAB • Display time-domain and frequencydomain representations of the selected window(s) Related Products For platform and system requirements, visit www.mathworks.
Statistics Toolbox 5 Apply statistical algorithms and probability models The Statistics Toolbox provides engineers, scientists, researchers, financial analysts, and statisticians with a comprehensive set of tools to assess and understand their data. It includes functions and interactive tools for analyzing historical data, modeling data, simulating systems, developing statistical algorithms, and learning and teaching statistics.
Probability Distributions Linear and Nonlinear Modeling The Statistics Toolbox includes interactive graphical user interfaces (GUIs) and command-line tools that make it easy to look at probability distributions, fit them to your data, or generate random samples from them. The linear and nonlinear models provided in the Statistics Toolbox let you model a response variable as a function of one or more predictor variables.
Analysis of Variance Analysis of variance (ANOVA) lets you determine whether data sets from different groups have different characteristics. You can classify groups using discrete predictor variables. A follow-up multiple comparisons analysis can pinpoint which pairs of groups differ from each other.
Left: A model of a chemical reaction of an experiment using the Design-of-Experiments and Industrial Statistics surface fitting capabilities of the The Statistics Toolbox provides a set of functions that support statistical process control (SPC). These functions enable you to monitor and improve products or processes by evaluating process variability. Designof- Experiments (DOE) functions help you create and test practical plans for gathering data for statistical modeling.
Sinar p3 V iew Camera In the system with the Sinar m
The Legendary Precision of Sinar View Cameras... Mechanical Precision True to the tradition of the Sinar p line of view cameras, the Sinar p3 too, is equipped with highly precise mechanisms. In keeping with the requirements of digital photography, the dimensions of the new camera have been significantly reduced without sacrificing any of this traditional precision.
...Combined with a State-of-the-Art Shutter... Sinar m for Modern The Sinar m can be seen as a follow-up model in the successful Sinarcam series. It provides a modern behind-the-lens shutter system for the use with the Sinar p3. The camera functions can be controlled either via the display on the Sinar m or using the Sinar CaptureShop™ capturing software.
...Optimized for the Digital Era. Intelligently Digital The Sinar p3 has been tailored uncompromisingly to function with Sinarback high-end digital backs. For this reason, electrical contacts have mostly been integrated into the camera, and the number of cables required reduced to a minimum. Depending on the type of Sinarback used, data transfer is carried out either via a modern firewire connection, or via a well-proven fiberoptic connection.
...Integrated in a System Geared to the Future.
Sinar – Better Images Thanks to Modularity and Innovation Perspective Correction and Sharpness Compensation Make life easy for yourself – with the adjustment capabilities of the Sinar p3 view camera. The generous adjustment range permits precise focusing, even with difficult subjects, thus preventing lengthy subsequent manipulation.
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The most modern technology for better photographs Sensor Developed by Kodak in cooperation with Sinar, the KAF22000CE sensor features a new color pigmentation for the best possible color stability and for a greater dynamic range. The combination of the large 38.8 mm x 50.0 mm optical format (a ratio of 4:3) of the sensor with 4080 x 5440 pixels with an edge length of 9/1000 mm leads to a truly sensational image quality.
Nothing can replace resolution... Difficult subjects in the 1-shot mode are captured with perfection by the Sinarback 54, thanks to its outstanding resolution, awardwinning color fidelity and active cooling of the sensor for the highest possible dynamic range. ...except more resolution Resolution with a sharp bite: The new Sinarback 54 makes photographs possible with unique sharpness, brilliance, in the rectangular format and with the detail fidelity of large format camera.
Technical Data Sinarback 54 Camera Platforms Sinarback Digital Backs • Digital camera back for exposures made with any kind of light: Sinarback 54 H: 1-, 4- and 16-shot Sinarback 54 S: 1-shot • Sensor dimensions: Sinarback 54: 49,0 mm x 36,7 mm • Sensor resolution: Sinarback 54: 5440 x 4080 pixels • Data sizes (at 16 bit TIFF): Sinarback 54: 1-shot 130 MB; 4-shot 130 MB; 16-shot 510 MB • Full color information without color interpolation in the 4- and 16-shot modes The new Sinarback 54 can be adapted to
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A truly modular camera system Fully automatic shutter system for the Sinar p3 view camera Regardless of whether it is used with Sinaron digital lenses, 35 mm camera lenses or medium format camera lenses, the Sinar m A digital camera for 35 mm, medium format, and Sinaron digital lenses fits perfectly in the hand – and with only a twist of the wrist it also serves perfectly as a shutter on the Sinar p3 view camera. The Sinar m has been optimally tailored for use with Sinarback digital backs.
Powerteam: Sinarback and Sinar m Sensible modularity ranging from a 35 mm camera all the way to a view camera A comprehensive, highly versatile system: The Sinar m offers an enormous spectrum of application possibilities and it is optimally integrated into the Sinar system. It is in combination with Sinarback digital backs that the new Sinar m shows its true greatness.
The technology of the Sinar m Straight-, prism- as well as zoom sports viewfinders can be used – exactly the right module for every task! Sinar m: Shutter speeds from 32 sec. to 1/4000 sec. – Size: 4.5 cm x 6.0 cm! Now there are no more limitations to outdoor photography with the Sinar m camera.
Calflex X™ Broadband Near-Infrared (NIR) Blocking Filter Unaxis Optics NIR-blocking filter Calflex X™ is commonly used to shield sensitive equipment such as silicon cells from harmful NIR-radiation. It combines extremely high NIRsuppression and sharp transition slope. Dielectric oxide coating design provides excellent transmission over the entire visible spectrum and sharp transition to reflection in the infrared.
