Assessment of multispectral and hyperspectral imaging systems for digitisation of a Russian icon

MacDonald et al. Herit Sci (2017) 5:41 DOI 10.1186/s40494-017-0154-1 RESEARCH ARTICLE Assessment ofmultispectral andhyperspectral imaging systems fordigitisation ofa Russian icon Lindsay W. MacDonald1*, Tatiana Vitorino2,3, Marcello Picollo3, Ruven Pillay4, Michał Obarzanowski5, Joanna Sobczyk5, Sérgio Nascimento6 and João Linhares6 Abstract In a study of multispectral and hyperspectral reflectance imaging, a Round Robin Test assessed the performance of different systems for the spectral digitisation of artworks. A Russian icon, mass-produced in Moscow in 1899, was digitised by ten institutions around Europe. The image quality was assessed by observers, and the reflectance spectra at selected points were reconstructed to characterise the icon’s colourants and to obtain a quantitative estimate of accuracy. The differing spatial resolutions of the systems affected their ability to resolve fine details in the printed pattern. There was a surprisingly wide variation in the quality of imagery, caused by unwanted reflections from both glossy painted and metallic gold areas of the icon’s surface. Specular reflection also degraded the accuracy of the reconstructed reflectance spectrum in some places, indicating the importance of control over the illumination geometry. Some devices that gave excellent results for matte colour charts proved to have poor performance for this demanding test object. There is a need for adoption of standards for digitising cultural heritage objects to achieve greater consistency of system performance and image quality. Keywords: Multispectral, Hyperspectral, Imaging, Digitisation, Reflectance, Spectrum, Specularity, Cultural heritage, Icon, Standards © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Introduction Scientific examination and accurate digital photographic recording of cultural heritage (CH) artworks are key for their contextualisation, documentation and conservation. Non-contact scientific examination may enable the identification of an artist’s materials and offers insight into the construction of an object without affecting its integrity. The information obtained expands knowledge about the production technology and visual appearance of the artwork, and the artist’s working methods. Precise and complete digital archival images also provide a definitive record of the object at the time of acquisition. If the resolution is high enough, the digital record also constitutes a means for the documentation and accessibility of CH [1–3]. For all of these purposes, accuracy and high quality are important aspects of the data acquired. Records should be true representations of the original artwork, without anything added or taken away. Acquisition of digital images and associated data serves as a good basis for repeatable research, across both time and place of different institutions, allowing the exchange and safe, non-destructive analysis of obtained results. Digital techniques enable a better and more precise description of all parameters during the acquisition and subsequent processing, including calibration and correction. Applications of spectral image acquisition systems include: imaging in broad bands by multispectral systems; quantification of reflectance spectra at each point by hyperspectral systems; recording of a data cube enabling the reconstruction of images (visible RGB, infrared, greyscale), and statistical processing for mapping, land usage, crop distributions, etc. Many types of spectral image acquisition systems have been applied for the study and digital documentation of cultural heritage artworks. Open Access *Correspondence: lindsay[email protected].uk 1 Faculty of Engineering Sciences, University College London, London, UK Full list of author information is available at the end of the article Page 2 of 16 MacDonald et al. Herit Sci (2017) 5:41 Depending on the device and workflow, however, the results obtained may vary considerably in quality. Colour and Space in Cultural Heritage (http://www. COSCH.info) was a European network of researchers, conservators and museum professionals, supported by the COST programme. This 4-year trans-domain Action (TD1201, 2013–2016) explored high-resolution optical techniques, defining good practice and open standards for digitisation and documentation of cultural objects. It was focused especially on practitioners of heritage science, i.e. technical staff with high levels of expertise in laboratories and university research facilities, who were associated with national museums. The primary objective of COSCH Working Group 1 (WG1) was the “identification, characterisation and testing of spectral imaging techniques in the visible and near infrared field”. In this context, a Round Robin Test (RRT) exercise was conducted to compare the characteristics and performance of different spectral imaging systems. Five test objects were analysed at 21 institutions, each with its own instrumentation and workflow. This article is focused on one of the RRT objects by qualitative comparison of the spectral imaging data obtained from ten different imaging systems. The multiple datasets were compared through visual inspection of both global and local areas, and the effectiveness of each system for dealing with this type of object was subjectively assessed. In addition, a reconstruction of the reflectance spectrum at selected points was undertaken. Although several systems in the complete RRT recorded infrared spectral reflectance of up to 2500nm, the ten systems reported in this article were limited to the visible and near infrared ranges, with a maximum wavelength of 1000nm. Test object: a Russian icon One of the RRT objects, the subject of this article, was a polychrome Russian icon from the late 19th century. Icons constitute a form of religious art, with stylised sacred images of saints, Christ and the Virgin Mary, in which the figures often appear timeless and static against golden backgrounds. The COSCH test object, hereafter described as the Russian icon, is cuboidal with dimensions 267mm×222mm×23mm. It was purchased through eBay in January 2014 from a dealer in Tallinn, Estonia. It depicts the traditional subject “Virgin of Kazan” but is printed by chromolithography, not painted by hand, onto a substrate of tinned steel. It gives the impression of the lid of an old biscuit tin, which has been nailed around the edges onto a wooden support. The surface is slightly convex, and its condition is marred by many tiny spots of rust on the underlying steel showing through the printed pattern. The icon was manufactured in Moscow in 1899 by the firm Zhako and Bonaker, whose speciality was making containers for shoe polish. The owners had invited master-painters from Mstera, a village in Vladimir Province, who were experts in the ancient techniques and styles of icon painting, to produce new designs. This capitalistic approach to mass-production had an adverse effect on the craft and artistry of icon painting, but the iconographic motifs, combined with surrogate gold (samovarnogo) and false jewels, made an indelible impression on the mass public, who purchased the products in their millions [4] (Fig.1). The Russian icon was chosen as a test object for the study because of its particular physical and material characteristics. Instead of a matte finish, it has a medium gloss on the painted areas and a shiny metallic finish on the gold. There is a wide range of tones and colours, including skin tones and a pictorial realism to the figures, which make it suitable for image quality assessment. The fine spatial detail in both the half-tone colour separations and the filigree gold decoration are appropriate for testing the optical resolving power of an imaging system. The surface is flat, almost planar, with virtually no 3D relief, and it has a convenient size and format. Because it is a common mass-produced object, rather than a fragile artwork, it can be handled freely by researchers in the laboratory without gloves. It turned out to be a very demanding test object, revealing several critical aspects of system performance. Fig. 1 Photograph of the Russian icon, manufactured in 1899 by the firm Zhako and Bonaker (Moscow), taken by a Nikon D200 camera on a copystand under tungsten illumination (for which see Fig. 13) Page 3 of 16 MacDonald et al. Herit Sci (2017) 5:41 Spectral reflectance image capture Spectral imaging systems, which scan the entire surface of an artwork without physical contact, enable spectroscopic information to be obtained as an accurate digital record. They have increasingly been included in the range of analytical techniques available for the conservation and study of CH [5–8]. Spectral imaging devices acquire images in many different wavebands, enabling the reflectance spectrum to be sampled at each pixel and hence the colorimetric representation of the image to be calculated for any specified spectrum of visible illumination. Note that in this study we are dealing with multi/hyperspectral imaging based on directional or diffuse reflectance of illumination from a surface as a function of wavelength. As an image acquisition modality, this is distinct from X-ray fluorescence scanning to obtain an XRF spectrum at every single point on an object, which could also be claimed as a type of hyperspectral imaging. An imaging system is commonly considered to be multispectral if the number of spectral bands is greater than 3 and less than 20, for which the working bandwidth is between 10 and 50nm. In turn, systems with hundreds of contiguous bands with a bandwidth less than 5nm are considered hyperspectral [9]. The definitions of multispectral and hyperspectral are very author-dependent. Goetz argues that the term multispectral should be used if no contiguous bands are acquired, irrespective of the range of spectral sensitivity of the equipment, while hyperspectral should apply if contiguous bands are acquired [10]. With this definition in mind, all of the equipment used in the present study may be considered as hyperspectral, some limited to the visible range and others covering the Visible+NIR range. Recently the new term ultraspectral has been proposed for systems where there are thousands of bands with a bandwidth of as little as 0.1nm [11]. All these image acquisition devices generate datasets with three dimensions (two spatial and one spectral), commonly represented as a data-cube [12]. Their performance, however, is dependent on the many components of the imaging system, including illumination, optics, filters, dispersive gratings, sensor and signal processing, and also on operator choices relating to object positioning, calibration, file format and metadata [13]. Usually, each institution has its own unique set-up and specific workflow, which are dependent on the working principles of the acquiring system, and hence can significantly affect the information acquired for the same object [14]. As an example, the standard procedure in the National Museum in Krakow, Poland, involves a series of tasks requiring manual adjustment and judgement, as follows: a. As there is no set working distance, first the area to be scanned is defined, bearing in mind that the larger the frame the coarser the spatial sampling. b. Focusing of the optics to obtain a sharp image. c. Placement of lights, setting the proper distance and angle (usually 45°). d. Setting exposure level to obtain maximum signal strength without over-exposure. e. Regulating movement speed of the camera stage to achieve true 1:1 geometric scale. f. Adjusting frame rate and sampling time for data acquisition. g. Recording the image. h. Recording data for image normalisation: white target scan and dark image scan. i. Applying a normalisation algorithm to the image data. j. Image data is saved in a standard format. k. Entry of metadata relating to image. This specificity not only makes it difficult to compare data from different systems, as it can influence the accuracy and reliability of the recorded information, but also causes some systems to be inappropriate for particular objects. Spectral imaging as a method of documentary recording of cultural artefacts would be much more useful if standardised procedures could be established for a wide range of systems, users and materials. Various earlier projects have contributed to the development of spectral imaging technology for the CH sector and have succeeded in designing high-performance hardware and software solutions for accurate image acquisition and processing. These commenced in the early 1990s with the VASARI project at The National Gallery, London, which achieved accurate high-resolution colorimetric images of paintings using a multispectral scanning system with seven filters in the visible region [15, 16]. The CRISATEL project later extended the work into the NIR region and applied basic spectroscopy techniques to the results [17]. Advances in technology made hyperspectral imaging possible in the 2000s [18]. The use of ‘pushbroom’ techniques was pioneered by IFAC-CNR in Florence [19, 20] and by the National Gallery of Art in Washington [21]. Simple photographic methods with transmission filters applied to either the camera or the illumination remain viable, however, and may be considered as the technological baseline against which more sophisticated systems can be compared [22]. Recent applications of hyperspectral imaging in cultural heritage include two Italian illuminated manuscripts, where the characteristics of the spectral reflectance signal in the visible range were used to identify pigments Page 4 of 16 MacDonald et al. Herit Sci (2017) 5:41 by comparing the obtained spectra with those of a reference library of medieval pigments [23]. In another study, the analytical suitability of two different hyperspectral imaging systems was evaluated for Goya’s paintings, the first employing a “push-broom” system and the second a “mirror-scanning” system. The main pigments present were identified by imaging reflection spectroscopy [24]. Qualitative assessment ofimage quality The research question within the present study was: to what extent do the operating principles and technologies of a range of spectral image capture systems affect the image quality achieved in the datasets? Spectral image quality is influenced by a number of factors [25] and understanding their role and how different devices are used in the digital documentation workflow will help to define better procedures for acquisition and processing. Spectral image recording systems are first and foremost imaging devices, and so can be compared against the performance of more familiar digital cameras and scanners. The quality of the images acquired can be assessed by the well-established methods of visual scaling and psychophysics, with observers judging each image either in isolation or side-by-side with a reference image [26]. Various attributes of image quality can be distinguished and scaled, including tonal rendering, colour, sharpness, naturalness, freedom from defects, etc. Image datasets from ten different systems were compared (Table 1). Four were pushbroom hyperspectral devices, three were multispectral systems based on liquid crystal tunable filter (LCTF), and three were Nikon cameras with external narrow-band transmission filters. The illumination system was different in each case. The institution in Spain carried out measurements with two different systems (D and J). Datasets from France, Italy, Poland and Cyprus were shared as file-cubes. All the others were provided as sets of single TIFF images or as Matlab data files. Because some systems are commercially available while others are custom-built and upgradable and flexible, it is difficult to compare them directly by specification. Nevertheless, the available information about the type of system, working bandwidth and spatial resolution is summarised in Table1. The spatial resolution is calculated as the number of pixels across the width Table 1 Participating institutions andcharacteristics oftheir systems Image Country System Range (nm) # Bands Working bandwidth (nm) Illumination source System Camera information Spatial resolution (pixels/mm) A Norway LCTF 400–720 33 10 Fluorescent lamps Custom built DVC-16000 M monochrome 4872 × 3248 6.0 B France Pushbroom 414–994 160 3.5 Tungsten halogen 150 W fibre optic Hyspex VNIR1600 SSI CCD 1600 × 1200 8.1 C Cyprus Filters 420–1000 30 20 Two 50 W halogen lights (4700 K) MuSIS HS CCD and photocathode 1600 × 1200 5.2 D Spain Pushbroom 400–1000 61 10 D65—SpectraLight III luminaire Custom built Monochrome CCD 1392 × 1040 12.0 E Poland Pushbroom 394–1009 776 <1 OSRAM Decostar WFL 12 V 35 W Specim V10E Monochrome sCMOS linear 2184 pixels 5.5 F Italy Pushbroom 398–961 448 <1 Two fibre-optic Schott 150 W Custom built Monochrome CCD 1344 × 1024 2.2 G Portugal LCTF 400–720 33 10 Metal Halide— Sylvania 150 W Custom built Monochrome CCD 1024 × 1344 4.1 H UK Filters 400–700 16 20 Photolux 150 W tungsten halogen Custom built Nikon D200 RGB CCD 3900 × 2600 10.4 J Spain LCTF 400–720 33 10 VeriVide Daylight Fluorescent Custom built Monochrome CCD 1392 × 1040 4.9 K Switzerland Filters 420–660 13 20 Two Broncolour flashlights Custom built Nikon D3 RGB CMOS 4256 × 2832 10.2 Page 5 of 16 MacDonald et al. Herit Sci (2017) 5:41 of the icon in the digitised image, divided by its physical width of 222mm. Colour images were initially reconstructed from each dataset using the ENVI 4.7 software. The three wavebands nearest to 605, 530 and 450nm were chosen for the red, green and blue (RGB) channels respectively to produce a ‘false colour’ image. Even though the responsivities of these channels are dependent on their bandwidths and peak wavelength, and are not equivalent to the spectral sensitivities of the eye’s photoreceptors nor to the primaries of the sRGB standard display [27], they offered a convenient means for qualitative comparison between the disparate imaging systems. Given sufficient time and resources, alternative methods would have been: (a) to make a weighted sum of the spectral bands to approximate the display RGB primaries; or (b) to reconstruct the visible reflectance spectrum for each pixel and then to calculate the sRGB image via the CIE tristimulus values (see Fig.9 below). A group of ten observers, five male and five female, with ages ranging from 20 to 50, all with normal colour vision, was chosen. They were asked to evaluate the quality of each of the ten reconstructed RGB images using a five-step psychometric (Likert-type) scale from 1 (completely disagree) to 5 (completely agree), for each of the following parameters: uniformity of illumination, contrast, sharpness, geometric distortion, visibility of fine spatial details, and colour fidelity. The participants were also asked whether they saw any defects in each image, and to evaluate the overall quality of the images on a five-step scale from 1 (very bad) to 5 (very good). Finally, they were asked to rank the ten images from most preferred to least preferred. As a reference image, the reconstructed RGB image from the dataset from Norway was chosen, as it was considered to lie somewhere between the best and worst images of the whole set. When shown to the observers, each of the other nine test images was displayed alongside the reference image (Fig.2). It would have been preferable to have judged the images against the actual icon, viewed under controlled illumination, but at the time of the experiment it was elsewhere being digitised. The diversity of imaging technologies from the different institutions created considerably different visual results, with varying degrees of pleasantness (Figs.2, 3, 4). The acceptability of the icon images was judged individually by ten observers, viewing them on an uncalibrated LCD display screen of diagonal 27 inches from a distance of approximately 1m. The display white point was set to D65 with a luminance of 180 cd/m2, and judgements were made under typical office illumination (combination of overhead fluorescent lights and daylight from westfacing windows). Each of the test images was compared side-by-side with the same reference image (Fig. 2A). It was not always a simple task to assess the differences between the images because in some cases they appeared dramatically different. Uneven illumination in some systems had caused shading effects and over-exposed areas due to specularity. Some images had been cropped to Fig. 2 RGB images of the Russian icon reconstructed from datasets of Norway (A) and France (B). The visual assessment always used the same image on the left as reference in a pair comparison technique Page 6 of 16 MacDonald et al. Herit Sci (2017) 5:41 different sizes and/or geometrically distorted, and varying resolution affected the definition of fine details. The following results emerged from the observer judgements: •A, C, H and J were considered the worst images in terms of uniformity of illumination, while B, F and G were considered the best; •B and K were considered the sharpest images, while J did not enable discrimination of fine details; •D and J were geometrically distorted (both skew and cropping); •F and G were considered the worst images in terms of colour reproduction; •B and K were the most preferred images, while J was the least acceptable. Fig. 3 RGB images of the Russian icon reconstructed from the datasets of: Cyprus (C), Spain (D), Poland (E) and Italy (F) Page 7 of 16 MacDonald et al. Herit Sci (2017) 5:41 The representation of image detail was assessed by enlarging a small square region of the image around the central jewel in the Virgin’s necklace, corresponding to an area of 10mm×10mm on the icon surface. Because of the differing spatial resolution of the systems (Table1), these details ranged in size from 22×22 to 120×120 pixels, but to enable fair comparison all have been enlarged to the same size (Fig.5). The variety of rendering of detail, as well as tone and colour, across the different systems is remarkable. It should be noted that the apparent sharpness of image detail depends not only on sampling resolution (pixels/mm) but also on the point Fig. 4 RGB images of the Russian icon from the datasets of: Portugal (G), UK (H), Spain (J) and Switzerland (K) Page 8 of 16 MacDonald et al. Herit Sci (2017) 5:41 spread function (focus) of the optics and registration of the images in the three colour channels. Spectral reconstruction A quantitative analysis was performed for the three hyperspectral datasets from France, Italy, and Poland, which were compared with the multispectral data from UK. Six points on the icon were selected (Fig.6) and the data vector was extracted from the corresponding pixels in each channel of each image using ENVI 4.7 software. The spectral reflectance distribution of the object at each point should ideally be independent of the characteristics of the imaging system and illumination and observer. To provide the ‘ground truth’ for reference, spot measurements were made at the six positions shown in Fig. 6 using a hand-held X-rite i1Pro spectrophotometer. This instrument is designed for quality control of glossy prints in the graphic arts, so has 45/0 geometry, i.e. it illuminates the surface at an angle of 45° and senses the light reflected perpendicular to the surface, in order to avoid specular reflections. The light is averaged over the area of the surface covered by a circular aperture of 4.5mm diameter. The reflectance factor is reported at 10nm intervals over the range 380–730nm, with results shown in Fig.7. The spectra for all colours are quite broad and smoothly changing. The gold was also measured, and was surprisingly low in reflectance factor because its lustre was excluded by the measurement geometry. It was not possible to measure the white or turquoise paints because nowhere on the surface was there an area large enough to span the instrument’s aperture. The reflectance spectrum at each location of multispectral dataset H was estimated by averaging a square region of 30×30 pixels in each of the 16 spectral channels, processing the image data in Matlab. As the image resolution was approximately 10 pixels/mm, this corresponds to a region of 3×3mm on the icon surface. The raw images from the camera (in NEF format) were converted to 3×16-bit RGB TIFF files by the utility DCRAW, to ensure tonal linearity of the data. Corresponding values were obtained for both the icon and a matte grey card used as the reference, and the reflectance factor R at each wavelength was calculated as: where filter k (range 1–16) corresponds to peak wavelength k = 400, 420, 440 ...700 nm; G is the reflectance factor of the grey card, measured by the spectrophotometer; i is the mean pixel value of the icon image in the sample area; g is the mean pixel value of the grey card image in the sample area; b is the mean pixel value of the black image in the sample area. All images were taken in a dark room to minimise the ambient light. The grey card was placed in the plane of the top surface of the icon, covering the same area, and served to correct both non-uniformity of illumination across the surface of the object and vignetting of the lens. R ( k )=G( k )(i( k )−b)/  g( k )−b  Fig. 5 Detail of the central jewel in the Virgin’s necklace from the ten reconstructed images of (left to right): Norway (a), France (b), Cyprus (c), Spain (d), Poland (e), Italy (f), Portugal (g), UK (h), Spain (j) and Switzerland (k) Page 9 of 16 MacDonald et al. Herit Sci (2017) 5:41 The black image was taken with the grey card in place but the copystand lights off, and was an average of ten successive image frames, to reduce the effects of noise. The blue camera channel was used for k in the range 1–6 (filter wavelengths 400–500nm), the green channel for k in the range 7–10 (520–580nm), and the red channel for k in the range 11–16 (600–700nm). The end values at 400 and 700nm were unreliable because of the very low signal level through the filters. The resulting reflectance factors are plotted in Fig.8 as a function of wavelength against the reference measurements from the spectrophotometer for three sample points. The same calculation was applied to every pixel in the image and the resulting reflectance factors interpolated to 5nm intervals over the range 400–700nm. Tristimulus values were calculated by multiplying the reflectance factor at each pixel by the spectral power distribution of the D65 illuminant and the responsivity functions of the CIE Standard Observer. The resulting image was converted to the sRGB display colour space [27], giving the result in Fig.9. Comparison with the conventional RGB photograph from the same Nikon D200 camera (Fig.1) shows that the computed colorimetric image is lower in contrast and colour saturation, but is a good match in hue. Comparison with the ‘rough and ready’ RGB image synthesised from three bands of the multispectral image set (Fig.4H) shows a great improvement in overall colour balance, but a loss of sharpness because of uncorrected chromatic aberration. The three hyperspectral systems from France (B), Poland (E) and Italy (F) all used a Spectralon tile as the white reference. Comparison of their reflectance spectra reconstructed at six sample points of the images (Fig.10), reveals that they are in general agreement, with similar shapes of the curves and inflections at the same wavelengths. The differences in amplitude are indicative of how each individual imaging geometry reacted differently to the non-Lambertian surface of the icon. Analysis of the spectra in Fig. 10 enabled a quantitative assessment, by comparing the reconstructed spectra of the four systems with the reference spectrophotometer data. Using the Matlab function interp1, all spectra were interpolated to 1nm intervals in the range of the visible spectrum from 400 to 700nm, and differences were calculated between corresponding points in terms of both root-mean-square error (RMSE) and colour difference ( E∗ ab ). The latter is the Euclidean distance between two stimuli expressed in the CIE L*a*b* colour space, and is scaled so that one unit of E∗ ab corresponds approximately to one just-noticeable difference (JND) [28]. For colour photography and print reproduction, values of E∗ ab less than 5 would generally be considered acceptable [29]. The results in Table2 show the target colour in CIELAB coordinates (referred to the D65 illuminant) of each of the seven sample points in Fig.6 and the corresponding differences for the four systems. In most cases the RMSE errors were less than 0.02 and colour errors were less than 5 E∗ ab although larger values occurred for the red robe and gold sample points. 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