The	   preservation	   of	   cultural	   heritage	   and	   treatment	   thereof	   are	   delicate responsibilities	   that	   demand	   the	   best	   possible	   technologies	   and	   extreme	   care	   to avoid	   any	   irreversible	   loss.	   It	   necessitates	   a	   deep	   understanding	   of	   constituent materials,	   along	   with	   the	   analytical	   methods	   and	   cutting-­‐edge	   technologies. Considering	   this	   direction,	   the	   goal	   of	   this	   dissertation	   is	   to	   promote	   the conservation	   procedures	   by	   providing	   an	   applicable	   workflow	   for	   spectral-­‐based pigment	   identification.	   The	   proposed	   pipeline	   is	   a	   novel	   and	   practical	   aid	   for museum	  conservators	  for	  many	  aspects,	  such	  as	  inpainting,	  treatment	  and	  archiving of	  artwork. Spectral-­‐based	   pigment	   identification	   algorithms	   rely	   on	   accurate	   spectral	   data,	   a subtractive	  mixing	  model	  and	  an	  effective	  unmixing	  algorithm.	  In	  this	  dissertation, the	   spectral	   data	   were	   obtained	   using	   a	   spectral	   image	   acquisition	   system	   as	   a feasible	  and	  non-­‐destructive	  technique.	  A	   liquid-­‐crystal	  tunable	  filter	  (LCTF)	  and	  a CCD	   camera	   were	   used	   for	   spectral	   measurement	   of	   the	   painting.	   The	   spectral accuracy	  and	  precision	  of	  the	  LCTF-­‐based	  spectral	  acquisition	  system	  were	  assessed and	   enhanced.	   Of	   the	   common	   factors	   affecting	   the	   acquisition	   performance, capturing	   geometry,	   LCTF	   angular	   dependencies	   and	   spectral	   characterization algorithm	  were	  new	  contributions	  to	  the	  traditional	  workflow. The	   complexity	   of	   subtractive	   mixtures	   limits	   the	   effective	   application	   of	   linear unmixing	   algorithms	   for	   pigment	   identification.	   Accordingly,	   a	   new	   linear modification	  of	  single-­‐constant	  Kubelka-­‐Munk	  theory	  was	  derived	  to	  enable	  the	  use of	   available	   linear	   spectral	   unmixing	   algorithms	   for	   paint	  mixtures.	   A	   selection	   of geometric	   and	   iterative-­‐based	   unmixing	   algorithms	   was	   applied	   to	   the	   LCTF spectral	  images	  in	  the	  subtractive	  mixing	  space	  using	  the	  defined	  subtractive	  linear model.	   Final	   sets	   of	   primary	   pigments	   were	   improved	   employing	   a	   pre-­‐existing database	   of	   common	   pigments	   as	   a	   tool	   for	   practical	   inpainting	   procedures.	   The pigment	  maps,	  showing	  the	  concentration	  of	  each	  pigment	  per	  pixel,	  and	  RMS	  error images	   were	   calculated	   after	   estimating	   the	   primary	   pigments.	   The	   estimation errors	   and	   the	   performance	   of	   the	   pigment	   selection	   algorithms	   were	   assessed using	  23	  validation	  paintings. Finally,	  the	  primary	  pigment	  information	  combined	  with	  multi-­‐spectral	  images	  from a	   commercial	   6-­‐channel	   imaging	   system	   was	   used	   for	   visualization	   and	   high-­‐ resolution	  spectral	  image	  reproductions.

Pigment Identification of Paintings Based on Kubelka-Munk Theory and Spectral Images

The present paper proposes a method for estimating surface reflection properties of an oil painting from the multi-band image data and compressing the data volume in image analysis/synthesis. First we show the improved multi-band imaging system. Next we estimate the surface properties, including surface normal, surface spectral reflectance, and reflection model parameters. Then we analyze statistically the whole data set of the estimated surface-spectral reflectances. The surface spectral reflectances are represented by a linear combination of a small number of basis spectral patterns. Use of the basis patterns leads to reducing the spectral data storage. The quality of this representation is examined based on both color reproduction accuracy and memory capacity. Finally the compressed data are combined for rendering images of the paintings under arbitrary conditions of illumination and viewing

Multispectral Analysis of Oil Paintings and Data Compression

Digitization and visualization are both of great importance for Cultural Heritage, for instance for the design of virtual galleries. Despite a lot of research, enabling a real-time walkthrough around complex digital copies still remains difficult and challenging in the general case due to the complexity of the measurement and to the amount of data that has to be dealt with. In this paper, we introduce a new dedicated pipeline for both digitization and realistic rendering of art paintings. We exploit the fact that geometrical variations over the canvas are generally small, yet not negligible from a visual point of view. Unlike most existing painting digitization systems, we thus propose to acquire both geometry and texture. Then, we render both as a whole by using, for the texture, an analytical model which is fitted from real measurements, and by using for the geometry a hybrid approach combining two relief rendering techniques according to the scale. This allows us to derive an efficient adaptive scheme guaranteeing fast rendering rates for all viewpoints. With our pipeline the painting's relief is well preserved, thus the rendering is of high quality, and in addition the final data representing the digital copies remain compact.

/pdf/a-pipeline-for-the-digitization-and-the-realistic-rendering-1nbao862eo.pdf

A pipeline for the digitization and the realistic rendering of paintings

One of the challenges when imaging paintings is recording total appearance, that is, the object’s color, surface microstructure (gloss), and surface macrostructure (topography). In this thesis, various systems were used to achieve this task, and a psychophysical paired comparison experiment was conducted to evaluate their performance. A pair of strobe lights arranged at 60° from the normal on either side of the painting captured color information where the strobes produced either directional or diffuse illumination geometry. By adding a third strobe, arranging them 120° apart annularly, and cross polarizing, diffuse color and surface normal maps were measured. A fourth strobe was added and the four lights were rearranged 90° apart annularly, capturing similar data. This system was augmented by two scanning linear light sources arranged perpendicularly, facilitating the measurement of spatially varying BRDF and specular maps. A laser scanner was used to capture surface macrostructure and was combined with the diffuse color maps from the four-light configuration. Finally, a dome illumination system was used with software iii developed by Conservation Heritage Imaging to produce color maps. In all, eight different configurations were achieved and used to image three small paintings with a range of appearance attributes. Twenty-five naive observers compared computer-graphic renderings to the actual painting and judged similarity in terms of total appearance, gloss/shininess, texture, and color. Although the rankings varied with painting, two general trends emerged. First, the four-light configuration with or without the independent laser scanning produced images visually equivalent to conventional strobe illumination. Second, diffuse illumination was always ranked lowest. Acknowledgements I would like to express my gratitude to the following sources for helping me finish my thesis during my study at the Munsell Color Science Laboratory: The twenty-five observers who took part in my psychophysical experiments for their valuable time and patience. Dr. Roy S. Berns, for his help, invaluable support, and guidance regarding my study and research. I especially am grateful for his patience and encouragement as I worked to complete my studies. Dr. James A. Ferwerda, for helping and guiding me through my internship in Paris this past summer. Dr. Mark D. Fairchild, for his insight in color science and offering advice during my studies. Dr. Dave R. Wyble, for sharing his knowledge of color measurement and offering set-up help during my experiment. Dr. Jinwei Gu, for providing me with helpful advice on my research. Val Hemink, for her warm hospitality and continuous help. Tongbo Chen, for his patience regarding the use of experimental algorithms, and my classmates: Farhad Moghareh Abed, Jennifer Kruschwitz, Rachel Schwen, Alicia Stillwell, and Adrià Forés Herranz for their inspiring ideas and in-class help. My friends Yiheng Cai, Jie Feng, and Weiping Yang for their kindness throughout this process. Jennifer Kruschwitz, Stephen Dolph and Jean Louise Mahar for helping me proofread my thesis. v All of the Munsell Color Science Lab faculty, staff, and students for teaching me a lot of interesting things, being my Rochester family, and helping make Rochester feel like home. The Andrew W. Mellon Foundation for their financial support of my research. My lovely “boyfriend” Wesley for staying with me all the time. Lastly, I would like to thank my husband, Weihua Sun, and our parents for their support and encouragement.

Systems Evaluation for Computer Graphics Rendering of the Total Appearance of Paintings

Multi-spectral imaging and spectral reflectance reconstruction can be used in cultural-heritage institutes to digitalize their collections for documentation purposes. It can be used to simulate artwork under any lighting condition, and to analyze colorants that were used. The basic idea of a multi-spectral imaging system is to sub-sample spectral reflectance factor, producing results similar to a spectrophotometer. The sampled data are used to reconstruct reflectance for the visible spectrum. In this thesis, a wide band multispectral camera was designed and constructed to achieve high spectral and color accuracy as well as high image quality. Noise propagation theory was introduced and tested. A seven channel bandpass filter set was modeled using Gaussian functions and optimized to yield high spectral and colorimetric reproduction accuracy as well as low colorimetric noise. Single and "sandwich" filters were selected from o -the-shelf absorption filters using the Gaussian bandpass filter model. Experiments were conducted to test the spectral, color and noise performance of the novel sandwich filters and compared with interference filters. The novel sandwich filters led to increased colorimetric accuracy along with a reduction colorimetric noise. This imaging system will be used as part of a recommended workflow for museum archiving, and will be an important addition to the spectral imaging capabilities at MCSL.

Design and Construction of a Multispectral Camera for Spectral and Colorimetric Reproduction

The paper proposes a method for the imaging and rendering of art paintings using a multi-band camera system. The surface shape of an art painting is considered as a rough plane rather than a three-dimensional curved surface. First. we present an algorithm for estimating the surface normal at each pixel point, based on a photometric stereo without using a rangefinder. Next, an algorithm is presented for estimating the spectral reflectance function from a set of pixel values acquired at different illumination directions. Then, the surface reflectance and normal data are used for estimating the light reflection properties. The Torrance-Sparrow model is used for model fitting and parameter estimation. Finally, an experiment using an oil painting is executed for demonstrating the feasibility of the proposed method.

Imaging and rendering of oil paintings using a multi-band camera

This paper describes a method for precisely estimating the surface properties of art paintings, including surface normal, surface spectral reflectance, and reflection model parameters. We use a multi-band camera system with six spectral channels. The surface of a painting is observed under different illumination directions by the camera. We use the Torrance-Sparrow model as a reflection model. The surface normal is estimated on the different illumination observations by the photometric stereo. The spectral reflectance is estimated on the multi-band data by the Wiener method. The model parameters are estimated on the statistical distribution of the specular component Finally, all estimates are combined for rendering various appearances of the painting under different illumination and viewing conditions.

Estimation of surface properties of art paintings using a multi-band camera

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Imaging and rendering of oil paintings using a multi-band camera

Estimation of surface properties of art paintings using a multi-band camera