Hi Graeme,
I tried to map artificially corrupted spectral readings to the original
ones, using a simple linear principal component regression in spectral
space, and IMO the results seem quite promising (for one particular
"medium" of course, i.e. for one paper/ink/printer/dpi/halftoning/etc.
combination).
"Artificially corrupted" means 1) Argyll FWA applied + 2) convolution
with a low-pass filter kernel in order to simulate a different bandwith
of the other instrument + 3) non-linear distortion along the wavelength
axis in order to simulate a bad wavelength calibration of the other
instrument. Eventuelly the "corrupted" readings differed from the
original ones by 11dE avg, 56dE max.
Still a linear PCR in spectral space, using 10...20 principal
components, like
% compute correction matrix M
% training set, measured with 2nd instrument
spectral_readings = [
...
];
% same chart measured with 1st (reference) instrument
reference_readings = [
...
];
% number of principal components
num_pc = 15;
[u,s,v] = svd(spectral_readings);
basis = v(:,1:num_pc);
M = basis * ((spectral_readings * basis) \ reference_readings);
% apply correction to spectral readings measured with 2nd instrument
estimated_reference_readings = readings * M;
was able to obtain corrected residual errors of less than 0.05dE avg
(and spectral reflectance residuals of 0.0005% RMS).
