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\begin{document}
% \SweaveOpts{concordance=TRUE}

\title{ {\Huge Estimating a Spectrum from its Response} \\ {\Large Inverse Colorimetry}}
\author{Glenn Davis  \url{    <gdavis@gluonics.com>}}
\maketitle
% \thispagestyle{fancy}

% Setup stuff.
<<setup, echo=FALSE, results="hide">>=
require("knitr",quietly=TRUE)
opts_chunk$set(fig.path="figs/ag2-", fig.align="center",
  fig.width=7, fig.height=7, comment="")
knit_hooks$set(output = function(x, options) {
  paste('\\begin{Soutput}\n', x, '\\end{Soutput}\n', sep = '')
})
options(width=90)
if(!file.exists("figs")) dir.create("figs")
@

\setcounter{figure}{0}

% ----------------------------------------------------------------------------
\section*{Introduction}

Given a ``material responder" (e.g. a scanner),
two materials that produce the same response from the responder
are called \emph{metamers} for that responder.
A given response typically has infinitely many metamers;
the goal of this \textbf{colorSpec} vignette is to demonstrate
the calculation of a ``good" metamer from a response.
Koenderink \cite{Koenderink} calls this topic \emph{inverse colorimetry}.
In the case that the estimated spectrum is a reflectance spectrum,
the topic is often called \emph{reflectance estimation} or \emph{reflectance recovery},
see Bianco \cite{bianco2010}.

The \emph{centroid method}, 
which is the default and the featured method in this package, 
computes the \emph{centroid} of the set of all the metamers (if any).
This centroid is computed in an infinite-dimensional function space 
and its definition is expounded further in Davis \cite{Davis2018}.
Also illustrated in this vignette is the \emph{TLSS method}, 
developed by Burns \cite{Burns2015}.

The featured function from \textbf{colorSpec} used in this vignette is \code{invert()}.
Some lengthy plotting functions are imported from the file \code{invert-help.R}.
<<packs, echo=TRUE, message=FALSE>>=
library( colorSpec )
source( "invert-help.R" )  # for plotOriginalPlusEstimates() and plotReparam3()
@


Make a ``material responder'' from Illuminant E and the CIE 1931 standard observer.
<<block0, echo=TRUE, message=TRUE >>=
wave  = 400:700
E.eye = product( illuminantE(1,wave), "material", xyz1931.1nm, wavelength=wave )
@
Compare the original and equalized responsivities after reparameterization,
see Figure 1.
<<fig1, echo=TRUE,  message=TRUE, fig.pos="H", fig.height=3, fig.width=9, out.width='1.0\\linewidth', fig.cap='(a) the original responsivities of `E.eye`, and their sum. (b) the reparameterization to interval [0,1].  (c) the equalized responsivities, and their sum.' >>=
specnames( E.eye ) = c('xE','yE','zE')
plotReparam3( E.eye )
@




% ----------------------------------------------------------------------------

\section{Comparison of Unequalized and Equalized Responsivities - Centroid Method}

For the centroid method both unequalized and equalized versions are available.
For the equalized version, a perfectly neutral reflectance spectrum
is estimated exactly (up to numerical precision).

Read reflectances from the standard NCSU collection, Vrhel \cite{Vrhel},
and select a subset of 6.
<<block1, echo=TRUE, message=FALSE >>=
NCSU = readSpectra( system.file( 'extdata/objects/NCSU.txt', package='colorSpec' ) )
NCSU6 = resample( subset( NCSU, c(18,32,54,62,142,170) ), wave=wave )
@
Compute responses (XYZ) for these 6 and then estimate spectra from the responses.
<<block2, echo=TRUE, message=FALSE >>=
XYZ = product( NCSU6, E.eye, wavelength=wave )
est.eq   = invert( E.eye, XYZ, method='centroid', alpha=1 )
est.uneq = invert( E.eye, XYZ, method='centroid', alpha=NULL )
@
Compare the original spectra, and the spectra estimated from the responses,
see Figure 2.
<<fig2, echo=TRUE, fig.pos="H", fig.height=4, fig.width=8, out.width='1.0\\linewidth', fig.cap='The original reflectance spectrum is solid linestyle; the equalized estimate is dashed; and the unequalized estimate is dotted.  Both estimates use the centroid method.' >>=
plotOriginalPlusEstimates( list(NCSU6,est.eq,est.uneq), ymax=0.9 )
@
Note that the unequalized (dotted) estimates have 2 artifacts:
undulations and tending to 0.5 at both ends.
The undulations are especially noticeable for \textsf{170 Cotton cloth -- Light gray}.
The equalized estimates (dashed) suppress the undulations 
and now both ends tend to flatten.



\section{Comparison of Centroid and Hawkyard Methods}

The Hawkyard method is explained in Hawkyard \cite{Hawkyard1993} 
and Bianco \cite{bianco2010}.
As in the centroid method, both unequalized and equalized versions are available.
Compare the equalized verions of both methods, see Figure 3.
<<fig3, echo=TRUE, message=TRUE, fig.pos="H", fig.height=4, fig.width=8, out.width='1.0\\linewidth', fig.cap='The original reflectance spectrum is solid linestyle; the centroid estimate is dashed; and the Hawkyard estimate is dotted.  Both estimates are equalized, and the illuminant is E.' >>=
est.c = invert( E.eye, XYZ, method='centroid', alpha=1 )
est.h = invert( E.eye, XYZ, method='Hawkyard', alpha=1 )
plotOriginalPlusEstimates( list(NCSU6,est.c,est.h) )
@
The estimates are similar, but note that for \textsf{142 Carrot} 
the Hawkyard estimate (dotted) was negative near 500nm and had to be clamped.
For this material, the Hawkyard estimate is not metameric to the original spectrum.




\section{Near Optimal Colors - Centroid Method}

An \emph{optimal color} (\emph{Optimalfarbe}) is generated by a
reflectance spectrum that only takes 2 values - 0 and 1.
The centroid method cannot invert an optimal color;
the numerical iteration fails to converge.
But it \emph{can} invert \emph{near}-optimal colors.
In this section all spectra take the 2 reflectance values - 0.01 and 0.99.

The first group is the near-\emph{edge colors} (\emph{Kantenfarben})
that have exactly one transition.
See Figure 4.
The estimates are good, except when the reflectance is concentrated near the ends,
where the responsivity of the eye is low.
<<fig4, echo=TRUE, fig.pos="H", fig.height=4.5, fig.width=8, out.width='1.0\\linewidth', fig.cap='The original reflectance spectrum is solid linestyle, and the centroid estimate is dashed. The light source is Illuminant E.' >>=
lambda = c(-Inf,450, -Inf,500, -Inf,550, 650,Inf, 600,Inf, 550,Inf )
nearedge = rectangularMaterial( lambda, 0.98, wave )  # chromatic amplitude = 0.98
XYZ   = product( nearedge, E.eye, wavelength=wave )
est.c = invert( E.eye, XYZ, method='centroid' )
# range( extradata(est.c)$estim.precis )    # est.c$estim.precis also works
plotOriginalPlusEstimates( list(nearedge,est.c), ymax=1.1 )
@
The next group is the near-optimals with exactly two transitions;
in \cite{West} these are called the \emph{Schr{\"o}dinger colors}
because they were studied by Schr{\"o}dinger \cite{ANDP:ANDP19203671504}.
<<fig5, echo=TRUE, fig.pos="H", fig.height=6.75, fig.width=8, out.width='1.0\\linewidth', fig.cap='The original reflectance spectrum is solid linestyle; the centroid estimate is dashed. The light source is Illuminant E.' >>=
lambda = c(600,450, 650,500, 450,500, 500,550, 550,600, 600,650 )
lambda = c( lambda, 500,525, 525,550, 550,575 )
nearschro = rectangularMaterial( lambda, 0.98, wave ) # chromatic amplitude = 0.98
XYZ = product( nearschro, E.eye, wavelength=wave )
est.c = invert( E.eye, XYZ, method='centroid' )
# range( extradata(est.c)$estim.precis )      # est.c$estim.precis also works
plotOriginalPlusEstimates( list(nearschro,est.c), ymax=1.1, mfrow=c(3,3) )
@
See Figure 5.
The method is now clearly having trouble with these extreme spectra.
It does best in the region around 500nm where the discrimination of the
responsivities is the best, see Figure 1.


\section{Fluorescent Light Source}

Now we turn from perfectly constant Illuminant E to a fluorescent illuminant,
which is as spiky as you can get.
Make a ``material responder'' from Illuminant F10 and the CIE 1931 standard observer.
Compare the original and equalized responsivities
<<fig6, echo=TRUE,  message=TRUE, fig.pos="H", fig.height=3, fig.width=9, out.width='1.0\\linewidth', fig.cap='(a) the original responsivities of `F10.eye`, and their sum. (b) the reparameterization to interval [0,1].  (c) the equalized responsivities, and their sum.' >>=
F10.eye = product( subset(Fs.5nm,'F10'), "material", xyz1931.1nm, wavelength=wave )
specnames( F10.eye ) = c('xF10','yF10','zF10')
plotReparam3( F10.eye )
@
Because the spectrum of \textsf{F10} is so spiky,
the equalized responsivities are much more irregular than they were for
Illuminant E, compare with Figure 2.
Now compute the XYZ responses, and
compare the original spectra, and the spectra estimated from the responses.
<<fig7, echo=TRUE, fig.pos="H", fig.height=4, fig.width=8, out.width='1.0\\linewidth', fig.cap='The original reflectance spectrum is solid linestyle, and the equalized estimate is dashed. The light source is Illuminant F10.' >>=
XYZ    = product( NCSU6, F10.eye, wavelength=wave )
est.eq = invert( F10.eye, XYZ, method='centroid', alpha=1 )
# range( extradata(est.eq)$estim.precis )    # est.eq$estim.precis also works
plotOriginalPlusEstimates( list(NCSU6,est.eq), ymax=0.9 )
@
Despite the irregularities in the equalized responsivities,
there is enough cancellation in the linear combinations that the estimated
spectra are not that irregular; compare with Figure 3.


\section{An Electronic Camera}

Now we turn from the 1931 Standard Observer to an electronic camera.
Make a ``material responder'' from Illuminant E and the \texttt{Flea2.RGB} camera.
Compare the original and equalized responsivities
<<fig8, echo=TRUE,  message=TRUE, fig.pos="H", fig.height=3, fig.width=9, out.width='1.0\\linewidth', fig.cap='(a) the original responsivities of E.Flea2, and their sum. (b) the reparameterization to interval [0,1].  (c) the equalized responsivities, and their sum.' >>=
E.Flea2 = product( illuminantE(1,wave), "material", Flea2.RGB, wavelength=wave )
specnames( E.Flea2 ) = c('rE','gE','bE')
plotReparam3( E.Flea2 )
@
Compute the RGB responses, 
and compare the original spectra and the spectra estimated from the responses,
see Figure 9.
The results are comparable to those with a human eye, see Figure 3.
<<fig9, echo=TRUE, fig.pos="H", fig.height=4, fig.width=8, out.width='1.0\\linewidth', fig.cap='The original reflectance spectrum is solid linestyle; the equalized estimate is dashed.  The light source is Illuminant E, and the camera is a Flea2.' >>=
RGB    = product( NCSU6, E.Flea2, wavelength=wave )
est.eq = invert( E.Flea2, RGB, method='centroid', alpha=1 )
# range( extradata(est.eq)$estim.precis )     # est.eq$estim.precis also works
plotOriginalPlusEstimates( list(NCSU6,est.eq), ymax=0.9 )
@
Now we turn to techniques for improving the accuracy of the estimate.
One classical technique is to keep the same camera, and add more light sources.
Here, we will add Illuminant A, which has lots of energy at long wavelengths,
and also add a black body radiator with a temperature of 9000 K,
which has lots of energy at short wavelengths.
There are now 9 responsivities, see Figure 10.
<<fig10, echo=TRUE,  message=TRUE, fig.pos="H", fig.height=3, fig.width=9, out.width='1.0\\linewidth', fig.cap='(a) the original responsivities of `E.flea`, and their sum. (b) the reparameterization to interval [0,1].  (c) the equalized responsivities using 3 different light sources, and their sum.' >>=
A.Flea2 = product( A.1nm, "material", Flea2.RGB, wavelength=wave )
specnames( A.Flea2 ) = c('rA','gA','bA')
P.Flea2 = product( planckSpectra(9000), "material", Flea2.RGB, wavelength=wave )
specnames( P.Flea2 ) = c('rP','gP','bP')
PEA.Flea2 = bind( P.Flea2, E.Flea2, A.Flea2 )
plotReparam3( PEA.Flea2 )
@
Compute the responses, and
compare the original spectra and the spectra estimated from the responses. 
Note that each response is now a 9-vector instead of a 3-vector.
See Figure 11.
<<fig11, echo=TRUE,  message=TRUE, fig.pos="H", fig.height=4, fig.width=8, out.width='1.0\\linewidth', fig.cap='The original reflectance spectrum is solid linestyle; the equalized estimate is dashed.  This estimate uses 3 different light sources.' >>=
response = product( NCSU6, PEA.Flea2, wavelength=wave )
est.eq   = invert( PEA.Flea2, response, method='centroid', alpha=1 )
# range( extradata(est.eq)$estim.precis )    # est.eq$estim.precis also works
plotOriginalPlusEstimates( list(NCSU6,est.eq), ymax=0.9 )
@
The accuracy of all materials is now improved over Figure 9,
except for \textsf{170 Cotton cloth -- Light gray}, which now has some undulations.
It appears that the extra accuracy in matching the dip below 425nm
has introduced these undulations.
There's a trade-off - oh well.


\section{Estimating Light Sources}

The centroid and TLSS methods also work for estimating light sources,
but the mathematics is now different because there is no upper limit constraint
on the energy spectrum of light.
Create an arbitrary sequence of light spectra with increasing 
correlated color temperature (CCT),
and then estimate the spectra from the XYZ response.
<<fig12, echo=TRUE,  message=TRUE, fig.pos="H", fig.height=5, fig.width=10, out.width='1.0\\linewidth', fig.cap='The original light source spectrum is solid linestyle; the centroid-based estimate is dashed, and the TLSS-based estimate is dotted.' >>=
eye   = resample( xyz1931.1nm, wave )
spec  = planckSpectra( c(3000,4000), wave )
spec  = bind( spec, daylightSpectra( c(5000,6504,9000), wave ) )
spec  = bind( spec, planckSpectra( 11000, wave ) )
XYZ   = product( spec, eye )
est.c = invert( eye, XYZ, method='centroid' ); est.t = invert( eye, XYZ, method='tlss' )
plotOriginalPlusEstimates( list(spec,est.c,est.t), mfrow=c(2,3), ymax=NA )
@
These estimates are poor, but the TLSS-based ones are somewhat better.
They tend to flatten out at the ends, while the originals do not.
% A better method might be to compute the CCT and then a Planckian or daylight
% spectrum from the CCT, as Koenderink has pointed out in \cite{Koenderink} page 544.
Moreover, there are some XYZ vectors for which the centroid method
does not work at all; for more on this see Appendix B and \cite{Davis2018} Section 13.
% I see little practical value in this type of inversion.


% \pagebreak

\bibliographystyle{plain}    % apalike
\bibliography{bibliography}


\section*{Appendix A - The Macbeth ColorChecker\texttrademark}
<<appendixCC, child='MacbethCC.txt'>>=
@



\section*{Appendix B - Inverse Colorimetry}
<<appendixRI, child='right-inverse.txt'>>=
@




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\section*{Session Information}
This document was prepared \today \hskip 0.5em\relax with the following configuration:
<<finish, echo=FALSE, results="asis">>=
knit_hooks$set(output = function(x, options) { x })
toLatex(sessionInfo(), locale=FALSE)
@


\end{document}
