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<title>Munipack ‒ Colour processing</title>
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<a class="headtitle" href="munipack.html">Munipack</a>
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<section>
<h1 class="noindent">Colour processing</h1>
<p class="abstract">
There are described basic principles and algorithms used
for colour processing.
</p>
<figure>
<img class="figure" alt="color-best.png" src="color-best.png">
</figure>
<p>
<a href="https://integral.physics.muni.cz/ftp/munipack/munipack-data.tar.gz" class="download">Sample data</a>
</p>
<h2>What's colour?</h2>
<p>
As a colour one can consider a perception of a spectral property of reflected
or illuminated light by the human eye. Our goal is to detect, to process and
to display pictures by a such way, which correctly reproduces colours
of the original scene.
</p>
<p>
The colour si derived from a spectral property of reflected
light by an object, while the colour vision is a product of processing
of the light by both the human eye and brain.
The right colour can't be determined objectively by an instrument,
it is fully determined by human perception itself.
</p>
<p class="indent">
The meaning of the colour in objective terms can be specified more precisely:
the colour is determined by proper composition of light bands transmitted
throughout exactly defined set of filters. The filters
approximates colour perception of receptors of human eye defined by
<a href="https://en.wikipedia.org/wiki/CIE_1931_color_space">CIE 1931 XYZ</a>.
By the description, the colour processing is focused to provide
the most close approximation of human colour perception.
</p>
<p class="indent">
The astronomical terminology is more vague. "Colour" means
a light flux in any filter, not necessary, in a filter appropriate
to human receptor sensitivity.
</p>
<h2>Colour reproduction</h2>
<p>
The common method of reproduction of colours is RGB colour model for
emitting devices. The RGB colour emitters has distinct
spectral sensitivity as human eye, original colours
should be transformed to the colour space.
</p>
<h2>Colour approximation</h2>
<p>
Munipack is designed to handle colours as
correctly as possible. There is effort to display of images
by colours perfectly reconstructing of the original scene.
The authenticity can be limited just only by the used hardware.
</p>
<p class="indent">
Light fluxes acquired by an instrument like an astronomical CCD camera
are usually different from ones of display devices. The display devices
uses sRGB (PC-type hardware) or Adobe RGB (Apple) opposite to Johnson
UBVRI colour system. Therefore, we need transform the colour data each other.
Without the transformation, the colours will strongly deformed.
</p>
<p class="indent">
The primary colour space of Munipack is CIE XYZ which is practically
colour space of the human eye.
</p>
<p class="indent">
Munipack can display only colour FITSes as is <a href="colourfits.html">specified.</a>
There is no widely accepted colour FITS definition so colour FITSes can be
created just only by Munipack utilities. Please, be consentient that the
definition can be changed at any time in future.
</p>
<h1>Colour processing</h1>
<p>
The colour processing is based on working in colour spaces. Internally,
Munipack uses
<a href="https://en.wikipedia.org/wiki/CIE_1931_color_space">CIE 1931 XYZ</a>
and
<a href="https://en.wikipedia.org/wiki/CIELUV_color_space">CIE 1976 L*u*v*</a>
colour spaces. An input data
in another colour space are transformed to CIE 1931 XYZ. The display is in a
<a href="https://en.wikipedia.org/wiki/RGB_color_model">RGB</a> space.
</p>
<p class="indent">
The colour processing in Munipack starts with loading of colour FITS.
The software automatically recognize the type of colour space by reading
of CSPACE keyword in FITS header.
</p>
<h2>Prescaling</h2>
<p>
When the type is different from XYZ, the data need to be transformed to
XYZ. In this case, the first step is prescaling of values. It is the
optional step, but usually required for best results. Main goal
of prescaling is to give the same flux from a white object in
different filters. Unfortunately, the fluxes are violated by
a different background and detector sensitivity or exposure time.
In light polluted industrial localities, the main source of
pollution are sodium lamps. The background for example in blue
or red filters is affected differently and we need light of the
object not, background. Munipack pre-estimate background levels
as the median subtracted by 1-sigma. The fine tuning needs an user
experience. The weight of every channel is not pre-estimated by any way.
The guide can be for example exposure time, but it may be also derived
from the telescope aperture, atmospheric conditions. etc. The
prescaling can be omitted (level =0, weight = 1 for all bands).
</p>
<p>
<i>
B'<sub>ij</sub> = w<sub>B</sub> (B<sub>ij</sub> - B<sub>0</sub>),<br>
V'<sub>ij</sub> = w<sub>V</sub> (V<sub>ij</sub> - V<sub>0</sub>),<br>
R'<sub>ij</sub> = w<sub>R</sub> (R<sub>ij</sub> - R<sub>0</sub>).
</i>
</p>
<div class="twocolumn">
<div class="column">
<figure>
<img src="colorB1.png" alt="colorB1.png" style="width:382px;" class="figure">
<figcaption>B<sub>0</sub> = V<sub>0</sub> = R<sub>0</sub> = 2000</figcaption>
</figure>
<figure>
<img src="color-best.png" alt="color-best.png" style="width:382px;" class="figure">
<figcaption>B<sub>0</sub> = 3700, V<sub>0</sub> = 9300,
R<sub>0</sub> = 20000</figcaption>
</figure>
</div>
<div class="column">
<figure>
<img src="colorB2.png" alt="colorB2.png" style="width:382px;" class="figure">
<figcaption>B<sub>0</sub> = V<sub>0</sub> = R<sub>0</sub> = 10000</figcaption>
</figure>
</div>
</div>
<figure>
<img class="figure" alt="" style="width:1043px" src="Screenshot-channels.png">
<figcaption>
The image has strong orange background due to sodium lamps.
One has been taken
<a href="http://maps.google.com/maps?f=q&source=s_q&hl=en&geocode=&q=Krav%C3%AD+Hora,+Brno,+Czech+Republic&sll=37.0625,-95.677068&sspn=56.375007,75.498047&ie=UTF8&hq=&hnear=Krav%C3%AD+hora,+Brno,+Czech+Republic&ll=49.204433,16.583582&spn=0.000719,0.001152&t=h&z=20">near of center of Brno town.</a>
The first image shows all colours as has been detected. The second
cut-offs blue. The last image has background level arranged according
to per-frame backgrounds. The structure above lighted sky are
clearly visible. This is common property of all urban observations.
</figcaption>
</figure>
<h2>Colour transformation</h2>
<p>
The colour transformation follows the prescaling.
The file <samp>ctable.dat</samp> is looked-up for the header's identifier.
When the colour space is found, the matrix is loaded and all data
are transformed. The transformation is usually done by matrix
multiplication. When the type remains unknown, the behavior is undefined
so colour assignment will random (false colours).
Note that the number of input colours
can be different from XYZ (tree colours).
</p>
<p>
<i>
X = a<sub>11</sub> B + a<sub>12</sub> V + a<sub>13</sub> R,<br>
Y = a<sub>21</sub> B + a<sub>22</sub> V + a<sub>23</sub> R,<br>
Z = a<sub>31</sub> B + a<sub>32</sub> V + a<sub>33</sub> R.
</i>
</p>
<h3>Before:</h3>
<div class="twocolumn">
<div class="column">
<figure>
<img src="colorB.png" alt="colorB.png" style="width:382px;" class="figure">
<figcaption>B</figcaption>
</figure>
<figure>
<img src="colorR.png" alt="colorR.png" style="width:382px;" class="figure">
<figcaption>R</figcaption>
</figure>
</div>
<div class="column">
<figure>
<img src="colorV.png" alt="colorV.png" style="width:382px;" class="figure">
<figcaption>V</figcaption>
</figure>
<figure>
<img src="color-best.png" alt="color-best.png" style="width:382px;" class="figure">
<figcaption>colour</figcaption>
</figure>
</div>
</div>
<h3>After:</h3>
<div class="twocolumn">
<div class="column">
<figure>
<img src="colorX.png" alt="colorX.png" style="width:382px;" class="figure">
<figcaption>X</figcaption>
</figure>
<figure>
<img src="colorZ.png" alt="colorZ.png" style="width:382px;" class="figure">
<figcaption>Z</figcaption>
</figure>
</div>
<div class="column">
<figure>
<img src="colorY.png" alt="colorY.png" style="width:382px;" class="figure">
<figcaption>Y</figcaption>
</figure>
<figure>
<img src="color-best.png" alt="color-best.png" style="width:382px;" class="figure">
<figcaption>colour</figcaption>
</figure>
</div>
</div>
<h1>Night Vision</h1>
<p>
When light intensity decreases, the effective of use of cones
is low and the (otherwise saturated) rod cell are activated. The
spectral sensitivity of rods is shifted to shot wavelengths with
respect to Y trisimulus. The transition region from daily (photopic
with cones as receptors) to
night vision (scotopic by rods) is mesotopic vision and the break
occurs around 0 magnitudes (10<sup>-2</sup> - 10<sup>-6</sup> cd/m<sup>2</sup>,
see <a href="https://en.wikipedia.org/wiki/Scotopic_vision">reference</a>)
for naked eye.
</p>
<p class="indent">
Munipack simulates the scotopic and mesotopic vision. The
scotopic sensitivity is approximated by the formula:
</p>
<p>
<i>I<sub>s</sub> = 0.362 Z + 1.182 Y - 0.805 X.</i>
</p>
<p>
Generally, the photopic, mesotopic and scotopic vision probably
operates simultaneously. The detailed mechanism is perhaps
unknown so the vision transition are simulated by the
(empirical estimation!)
<a href="https://en.wikipedia.org/wiki/Logistic_function">logistic function.</a>
The logistic function drives many similar effects in real world.
Especially, phenomenons of saturated detectors are frequently described
by the way.
</p>
<p>
<i>w = 1/(1 + exp(-x),</i><br>
<i>x = (I<sub>s</sub> - I<sub>t</sub>)/w<sub>t</sub>.</i>
</p>
<p>
Then output colours are computed as:
</p>
<p>
<i>X' = w X + (1-w) I<sub>s</sub>,</i><br>
<i>Y' = w Y + (1-w) I<sub>s</sub>,</i><br>
<i>Z' = w Z + (1-w) I<sub>s</sub>.</i>
</p>
<p>
The break must be setup manually and the the both vision are mixed.
The parameters Threshold <i>I<sub>s</sub></i> and Mesotopic
<i>I<sub>s</sub></i> are used. The thresholds sets
the level corresponds to the zero-magnitude break. The mesotopic
sets wide of transition region. This is absolutely empirical value
and depends on vision and detector's gain.
</p>
<p>
The weight determines the types of vision:
</p>
<ul>
<li>photopic: <i>w = 0</i></li>
<li>mesotopic: <i>0 < w < ∞ </i></li>
<li>scotopix: <i>w →∞</i></li>
</ul>
<!--
* popsat zavedeni logisticke funkce a koeficinetu 2.5
* napsat, ze reakce na svetlo v tehle oblasti nejsou znamy
* modelovani spociva v tom, ze potlacujeme vliv jednech a zdruaznujeme
v liv druhejch receprotu
* pozor na horsi rozliseni pri pohledu v ruznych (malych) dalekohledech
</p>
-->
<figure>
<img class="figure" alt="color-night.png" src="color-night.png">
<figcaption>
Picture in mesotopic regime. The threshold sets background to scotopic
and foreground to photopic vision. The setting corresponds to use of a
telescope with one meter diameter.
</figcaption>
</figure>
<figure>
<img class="figure" alt="color-scotopic.png" src="color-scotopic.png">
<figcaption>
Picture in scotopic regime. The red hydrogen shock waves are invisible.
One correspond to vision by a small telescope.
</figcaption>
</figure>
<figure>
<img class="figure" alt="Screenshot-nite.png" style="width:1043px" src="Screenshot-nite.png">
</figure>
<!--
<h2>Saturated colours</h2>
<p>
When any from input pixels are out of range, the colours are saturated.
In principle, there are two kind of saturation:
* relative saturation given just only limiting range of display and scaling,
photometry information is saved
* saturated detector when a object id too bright and detectors is saturated
</p>
-->
<h1>Colour tuning</h1>
<p>
The XYZ colour space corresponds to eye's precipitation of colour light
by the cones. The XYZ has no upper limit. The numbers must be zero
or positive. Unfortunately, the human perceptions of light intensity
and colours is not linear. Therefore to get tunable parameters
the parameters are transformed to CIE Luv colour space. The colour space
is used to tune parameters saturation, hue and to scale luminosity.
</p>
<h2>Saturation</h2>
<p>
The saturation parameter enable decrease or increase colours.
</p>
<p class="indent">
The saturation is practically multiplier of radius of colour
in <i>u,v</i> coordinates.
</p>
<div class="twocolumn">
<div class="column">
<figure>
<img src="colorS1.png" alt="colorS1.png" style="width:382px;" class="figure">
<figcaption>saturation = 1.5</figcaption>
</figure>
<figure>
<img src="colorS2.png" alt="colorS2.png" style="width:382px;" class="figure">
<figcaption>saturation = 0.5</figcaption>
</figure>
</div>
<div class="column">
<figure>
<img src="color-best.png" alt="color-best.png" style="width:382px;" class="figure">
<figcaption>saturation = 1.0</figcaption>
</figure>
<figure>
<img src="colorS3.png" alt="colorS3.png" style="width:382px;" class="figure">
<figcaption>saturation = 0</figcaption>
</figure>
</div>
</div>
<h2>Hue</h2>
<p>
Hue rotates the pixel in colour space and it is probably useless.
</p>
<p class="indent">
The hue is an angle added to angle of current colour in <i>u,v</i> coordinates.
</p>
<h2>White point</h2>
<p>
The white point parameters enable user to fine tuning of white on image.
Note that the white is also given by colour temperature. The ideal
object for white tuning are cumulus clouds. They are easy available
and white is excellent (tested on white etalon).
</p>
<div class="twocolumn">
<div class="column">
<figure>
<img src="color-best.png" alt="color-best.png" style="width:382px;" class="figure">
<figcaption>u = 0.5, v = 0.5</figcaption>
</figure>
<figure>
<img src="colorW3.png" alt="colorW3.png" style="width:382px;" class="figure">
<figcaption>u = 0.8, v = 0.6</figcaption>
</figure>
</div>
<div class="column">
<figure>
<img src="colorW2.png" alt="colorW2.png" style="width:382px;" class="figure">
<figcaption>u = 0.1, v = 0.8</figcaption>
</figure>
<figure>
<img src="colorW4.png" alt="colorW4.png" style="width:382px;" class="figure">
<figcaption>u = 0.8, v = 0.1</figcaption>
</figure>
</div>
</div>
<figure>
<img class="figure" alt="Screenshot-colors.png" style="width:1043px" src="Screenshot-colors.png">
</figure>
<h2>Output Colour space</h2>
<p>
Finally, the Luv is converted back to XYZ and consequently
XYZ to a RGB space. There are two possibilities. The
<a href="https://en.wikipedia.org/wiki/SRGB">sRGB</a> colour space is
widely used on PC-like hardware. If you are running Linux or Windows
your monitor, LCD or beamer works in sRGB.
The <a href="https://en.wikipedia.org/wiki/Adobe_RGB_color_space">AdobeRGB</a> is very similar
(a slightly different parameters are used) and is used on Apple hardware.
Note that the AdobeRGB has wider gamut (displays more colours). Your
RGB colour space must correspond to your HW, otherwise the output
colours will certainly deformed.
</p>
<p class="indent">
The tuning of colour space is available in Preferences. The colour temperature
must exactly corresponds to values set on your display.
</p>
<div class="twocolumn">
<div class="column">
<figure>
<img src="color-best.png" alt="color-best.png" style="width:382px;" class="figure">
<figcaption>sRGB</figcaption>
</figure>
</div>
<div class="column">
<figure>
<img src="colorAdobe.png" alt="colorAdobe.png" style="width:382px;" class="figure">
<figcaption>AdobeRGB</figcaption>
</figure>
</div>
</div>
<p>
No other colour spaces are available, but ones might be easy implemented
when needs.
</p>
<h1>Why so complicated?</h1>
<p>
The standard image formats stores data in very limited
precision of 256 levels on every colour. It is 256*256*256
approximately 17 millions colours. But low-cost CCD chip
has dynamic rage more wider and human eye at least over
ten orders. The main problem of displaying of astronomical
images is correct displaying of the wide range of data on display
with 256 levels. The problem is widely known in recent as the high
range photography HDR.
Moreover, the data has more usage over displaying.
For example, the photometric data requires high precision.
</p>
<p class="indent">
Therefore, the best way how to store of data is store raw data with
exact definition of photometric instrument (filters, etc.) and
use this data by various ways. One from the ways, it can be the colour
imaging. The side effect of the way are wide possibilities of image
tuning.
</p>
<h1>The algorithm</h1>
<p>
There is described algorithm used to rendering of colour FITS images.
</p>
<ol>
<li>Detect input colour space as CSPACE keyword in FITS header.</li>
<li>Processing:<br>
<ol>
<li>For general colour space, scale values and convert to CIE 1931 XYZ</li>
<li>Convert CIE 1931 XYZ to CIE Lab</li>
<li>Scale luminosity, tune saturation and hue.</li>
<li>Convert back from CIE Lab to CIE 1931 XYZ</li>
<li>Optionally add night vision.</li>
<li>Convert to a display RGB colour space</li>
</ol>
</li>
<li>Display image</li>
</ol>
<p>
The rendering code is implemented in C (<samp>fitspng.c</samp>),
Fortran (<samp>colouring/colour.f08</samp>) and
C++ (<samp>xmunipack/fitsimage.cpp</samp>).
</p>
<h1>Colouring tool</h1>
<p>
The colouring tool is invoked from the menu of View:
<samp class="sans">File → Create → Colour image</samp>
</p>
<figure>
<img class="figure" style="width:1043px" alt="Screenshot-Coloring.png"
src="Screenshot-Coloring.png">
</figure>
<h1>Command line usage</h1>
<p>
Complete colour management can be driven from a command line. There are
two Munipack internal utility colouring providing its.
The export from
colour FITS to any conventional picture format PNG is provided by
<a href="https://integral.physics.muni.cz/fitspng">fitspng</a> utility.
</p>
<h2>Colour composition</h2>
<p>
Composing of images to a colour image is provided by colouring internal
utility. It is invoked via <samp>munipack</samp> command. Use the syntax:
</p>
<pre>
$ munipack colouring -o colour.fits blue.fits green.fits red.fits
</pre>
<p>
Prepare pictures in a colour space and pass ones in wavelength increase order
to create a colour FITS. The test data package contains pictures of Dumbbell
nebula in Johnson BVR. The colour image can be created as:
<pre>
$ munipack colouring -o m27.fits m27_B.fits m27_V.fits m27_R.fits
</pre>
<h3>Acknowledgment</h3>
<p>
Images of M27 has been taken by J.Połedniková, O. Urban and M. Kocka
on MonteBoo observatory via 0.62 m reflector.
</p>
</section>
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© 1997 – 2025
<a href="https://integral.physics.muni.cz/" title="author's homepage"
class="foot">Filip Hroch</a>
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