Hyperspectral Imaging
applications in art and archaeology
PRESENTING:
OMER PAPARO
JANUARY 2013
Agenda
 Introduction


Motivation
Limitations
 Spectral imaging systems
 Pigment identification

The Kubelka-Munk theory of reflectance
 Investigating materials present on artifacts
 Revealing hidden information


In paintings
Studying archaeological manuscripts
 Art conservation


Conserving paintings
Best illuminants for viewing art
Introduction
 Motivation
 Traditional methods are often invasive


E.g., micro-chemical analysis of images
HI is non invasive
Can be carried out essentially on any object
 Can be carried out anywhere on an object

 Limitations
 Requires exposure to light


Some artifacts suffer light-induced ageing
Not always as accurate as traditional methods

Invasive chemical analyses, for instance, almost always yield more
chemically specific information
Spectral imaging systems
 Illumination

Light exposure must be kept at a minimum (both duration and
intensity)
Assuming the reciprocity principle
 ~200 lux for oil paintings, ~50 lux for manuscripts

 Wavelength selection

Wavelength selection through illumination


Only a selected wavelength range of light is incident on the object at a
time
 Economic exposure, yet sensitive to background light
Wavelength selection in the reflected light
Light reflected from the object can be separated spectrally
 Can collect spectral data sequentially or simultaneously

 Detector
Spectral imaging systems
 Working scheme:
Spectral imaging systems
 Measurement at the Uffizi Gallery, Florence, Italy -
Leonardo room
Pigment identification
 Introduction – What are paintings made of?
 A pigment is a colored material ground into a fine powder


After the grinding it is suspended in some type of media that acts
as a binder to hold the dry pigments pigment together
 E.g. linseed oil for oil paints
Over the eras, many different pigments were used
Pigment identification
 E.g., the late gothic palette
Pigment identification
 E.g., the late Italian Renaissance palette
Pigment identification
 The main challenge is unmixing measured
reflectance to separate reflectances of different
materials

Linear unmixing won’t work here – the mixing is not linear
(materials can be mixed almost to atomic level)
Pigment identification
 Measuring reflectance is relatively easy
 Suppose we’ve measured for some pixel, for same wavelength
𝐼
𝜆, the reflectance 𝑅 =
𝐼
0
The ratio between the outgoing light and the incoming light
 Now what?


That reflectance, R, must be a combination of reflectances of
more than one material found in that pixel

But how can we separate them?
 Maybe the combined reflectance is a linear combination of those
reflectances?
• Well, not exactly

Introducing the Kubelka-Munk theory of reflectance
Pigment identification
 The Kubelka-Munk Theory of Reflectance:
 −𝑑𝑖 𝑇 = −(𝑆 + 𝐾)𝑖 𝑇 dx +𝑖𝑅 Sdx
 𝑑𝑖𝑅 = −(𝑆 + 𝐾)𝑖𝑅 dx +𝑖 𝑇 Sdx

Where K is the
absorption coefficient
and S is the
scattering coefficient
Pigment identification
 The Kubelka-Munk Theory of Reflectance (cont’d):

It thus can be achieved that
𝑑𝑖𝑅
𝑖𝑅
−
𝑑𝑖𝑇
𝑖𝑇
= d 𝑙𝑛
𝑖𝑅
𝑖𝑇
= −2(𝑆 +
Pigment identification
 The Kubelka-Munk Theory of Reflectance (cont’d):

Rearranging and integrating we get:
Solving this yields 𝑙𝑛

and 𝑏 = 𝑎2 − 1
So assuming:
𝑅−𝑎−𝑏
𝑅−𝑎+𝑏
∗
𝑅′ −𝑎+𝑏
𝑅′ −𝑎−𝑏
𝑅
𝑑𝑟
𝑅′ 1+𝑟 2 −2 𝑆+𝐾 𝑟
𝑆
=𝑆
= 2𝑏𝑆𝑋, where 𝑎 =
𝑋
𝑑𝑥.
0
𝑆+𝐾
𝑆
𝑅′ = 0 (no light gets to the back)
 𝑋 → ∞ (particle sizes are much smaller than the thickness of the
layer)

We can achieve that
𝐾
𝑆
=
1−𝑅∞ 2
2𝑅∞
Pigment identification
 The Kubelka-Munk Theory of Reflectance (cont’d):
 Other than cases in which the absorption is very high or the
scattering is very low, a mixture of different paint components
can be modeled as a linear combination of K/S (weights are
according to concentrations)
 Can predict components of mixture!

Graph shows mixture of
read earth and azurite
in egg tempera
 KM can fail
 E.g., in the mix of pure indigo and orpiment
 Would not have failed if the indigo was mixed with lead white
Pigment identification
 But generally, KM is robust
 Can handle varying:
Concentrations
 Binding medium
 Particles size

Investigating materials present on artifacts
 Similarly to pigment identification, we can perform
analysis on 3D objects

E.g., exploring the surface of
Michelangelo's David

Basically the sculpture is made of
marble, but over the years some
“guests” have joined
Investigating materials present on artifacts
 Collecting and analyzing the data
 A UV (𝜆 = 337 nm) excitation light is provided by a nitrogen
laser that generates 1 ns pulses


Assuming mono-exponential behavior of the fluorescent
−𝑡
emission, f, we get that 𝑓 𝑡 = 𝐴𝑒 𝜏 (per pixel)


N pulses are delivered and the emission is measured
Where 𝐴 is the amplitude and 𝜏 is the effective lifetime
Given the pulse was provided with delay d, we can acquire the
𝑑+𝑤
−𝑑
−𝑑+𝑤 𝜏
𝜏
fluence: 𝐻 𝑑 = 𝐶 𝑑
𝑓 𝑡 𝑑𝑡 = 𝐶𝐴𝜏 𝑒
−𝑒

Where 𝑤 is the gate width and 𝐶 is a constant dependent on the
efficiency of the detection system
Investigating materials present on artifacts
 Collecting and analyzing the data (cont’d)
 The effective lifetime 𝜏 and the amplitude 𝐴 can be
reconstructed by least mean squares fit performance on N time
samples:

Can build matrices of τ and 𝐴
Investigating materials present on artifacts
 Results
 Spectral signature is different than the one of “pure marble”
Investigating materials present on artifacts
 Results (cont’d)
 Can identify organic compounds
Investigating materials present on artifacts
 Results (cont’d)
 Can identify remains of beeswax

David’s surface underwent a
conservation treatment
based on beeswax in 1813
Revealing hidden information
 For paintings:
 Maximum penetration of most paints can be achieved at
wavelengths of around 2 μm
 At wavelengths around 1-2 μm, the common drawing
materials, namely iron gall ink and sepia, become invisible
 Can use this to see underdrawings and preparatory sketches
Revealing hidden information
 A Byzantine icon at 640nm (a) and 1000nm(b)
Revealing hidden information
 Pablo Picasso –
“The Tragedy”
Revealing hidden information
 The optimal spectral window to visualize such
features varies with the material used as well as the
thickness of the paint layer
Man, ~1100nm
Horse, ~1350nm
Sketch, ~1600nm
Revealing hidden information
 A painting by Sellaio
520nm
885nm
RGB
Revealing hidden information
Revealing hidden information
 Studying archaeological manuscripts
 “Soft media” ancient documents (i.e. documents written on
soft materials such as leather or papyrus) are often unreadable
The carbon-black ink is faded beyond recognition
 The carbon-black ink indistinguishable from the surface
 Not to mention the document itself is found in shreds

Revealing hidden information
 Studying archaeological manuscripts
 Can use IR to read previously invisible texts and scripts

The dead sea scrolls can only be seen through IR light
Art conservation
 Conserving Paintings
 Can fix damage using hidden information revealing techniques

The color image is derived from inter-band comparisons
Art conservation
 Conserving Paintings (cont’d)
 Conservation monitoring

Can identify continual damage to paintings, for example
 From a lamp in front of the painting
 From a pipe going through the ceiling
Art conservation
 Best illuminants for viewing art
 Which one looks better?
Art conservation
 Best illuminants for viewing art (cont’d)
 An illuminant for
appreciating art is
considered better
if number of
discernible colours
is greater
 Illuminants are
measured in
degrees Kalvin
Art conservation
 Best illuminants for viewing art (cont’d)
 The experiment:

1. Collect hyperspectral data from five different paintings
Art conservation
 Best illuminants for viewing art (cont’d)
 The experiment:
2. Calculate the illuminant spectra
 3. Compute the painting representation in CIELAB

Art conservation
 Best illuminants for viewing art (cont’d)
 The experiment:

4. Count the number of non-empty unit cubes in the CIELAB
space, and select best illuminant
Concluding
 Today we have seen:
 Basic structures of spectral imaging systems for art and
manuscripts
 Uses for hyperspectal imaging in art and archeology:
Identifying pigments used for paintings
 Investigating materials present on artifacts
 Viewing underlying sketches for paintings
 Studying old and corrupted-by-time documents
 Conserving art
 Protecting art from harm
 Viewing in with best illuminant

References
 H. Liang - Advances in multispectral and hyperspectral imaging for






archaeology and art conservation – 2011
C. Fischer and I. Kakoulli - Multispectral and hyperspectral imaging
technologies in conservation: current research and potential
applications – 2006
J. K. Delaney et al - Visible and Infrared Reflectance Imaging
Spectroscopy of Paintings: Pigment Mapping and Improved
Infrared Reflectography – 2009
F. Voltolini et al - Integration of non-invasive techniques for
documentation and preservation of complex architectures and
artwork
J.A. Carvalhal et al - Estimating the best illuminants for
appreciation of art paintings
G.H. Bear-man et al - Archeological Applications of Advanced
imaging Techniques
D. Comelli et al - Fluorescence Lifetime Imaging and Fourier
Transform Infrared Spectroscopy of Michelangelo’s David - 2005
Thank you
 Questions?