[gnuastro-commits] master cf40daba 13/23: Book: edited tutorial on color image production

[Mohammad Akhlaghi]

branch: master
commit cf40dababdfd09d586b47910bcb0017ce60b9fbc
Author: Mohammad Akhlaghi <mohammad@akhlaghi.org>
Commit: Mohammad Akhlaghi <mohammad@akhlaghi.org>

    Book: edited tutorial on color image production
    
    Until now, the newly added color image tutorial hadn't been edited by
    someone other than the author (Raul)!
    
    With this commit, I went through it and made some minor corrections to make
    it clear. Also, the output of '--help' from the script was edited to follow
    the Gnuastro standards (only single line descriptions that are shorter than
    80 characters).
---
 bin/script/rgb-faint-gray.sh |  22 ++---
 doc/gnuastro.texi            | 200 ++++++++++++++++++++-----------------------
 2 files changed, 103 insertions(+), 119 deletions(-)

diff --git a/bin/script/rgb-faint-gray.sh b/bin/script/rgb-faint-gray.sh
index d4fcffe2..bb04fa31 100644
--- a/bin/script/rgb-faint-gray.sh
+++ b/bin/script/rgb-faint-gray.sh
@@ -123,9 +123,8 @@ experienced Gnuastro users and developers. For more 
information, please run:
 
 $scriptname options:
  Input:
-  -h, --hdu=STR           HDU/extension for the inputs (R,G,B,K) channels.
+  -h, --hdu=STR           HDU/extension for the input channels.
   -g, --globalhdu=STR/INT Use this HDU for all inputs, ignore '--hdu'.
-
   -w, --weight=FLT        Relative weight for each input channel.
   -m, --minimum=FLT       Minimum value for each input channel.
   -z, --zeropoint=FLT     Zero point magnitude of each input channel.
@@ -137,18 +136,17 @@ $scriptname options:
  Contrast and brightness
   -b, --brightness        Change the brightness of the final image (linear).
   -c, --contrast          Change the contrast of the final image (linear).
-  -G, --gamma             Gamma parameter for gamma transformation (non linear,
-                          this overrides --brightness or --contrast)
+  -G, --gamma             Gamma parameter (overrides --brightness/--contrast).
 
  Color and gray parameters
-      --black               Generate the black-background color image.
-      --grayval=FLT         Value that defines the black and white (for gray 
regions).
-      --colorval=FLT        Value that defines the separation between color 
and black.
-      --graykernelfwhm=FLT  Kernel FWHM for convolving the background image.
-      --colorkernelfwhm=FLT Kernel FWHM for convolving the reference image 
that is used
-                            for defining the separation between the color and 
black parts.
+      --black             Generate the black-background color image.
+      --grayval=FLT       White threshold (fainter values will be white).
+      --colorval=FLT      Color threshold (larger values will have color)
+      --graykernelfwhm=FLT Kernel FWHM for convolving the background image.
+      --colorkernelfwhm=FLT Kernel FWHM for color separation ref. image.
+
  Output:
-      --checkparams       Print the distribution of values for obtaining the 
parameters.
+      --checkparams       Print distribution of values used to find params.
   -k, --keeptmp           Keep temporal/auxiliar files.
   -o, --output            Output color image name.
 
@@ -1066,5 +1064,3 @@ fi
 # The script has finished, reset the original language to the system's
 # default language.
 export LANG=$system_lang
-
-
diff --git a/doc/gnuastro.texi b/doc/gnuastro.texi
index 0ac16eda..cf15d3b4 100644
--- a/doc/gnuastro.texi
+++ b/doc/gnuastro.texi
@@ -272,11 +272,11 @@ Tutorials
 * Building the extended PSF::   How to extract an extended PSF from science 
data.
 * Sufi simulates a detection::  Simulating a detection.
 * Detecting lines and extracting spectra in 3D data::  Extracting spectra and 
emission line properties.
-* Creating color images::
+* Creating color images::       Color images that show faint and bright parts 
of source nicely.
 * Zero point of an image::      Estimate the zero point of an image.
 * Pointing pattern design::     Optimizing the pointings of your observations.
 * Moire pattern in stacking and its correction::  How to avoid this grid-based 
artifact.
-* Clipping outliers::  How to avoid outliers in your measurements.
+* Clipping outliers::           How to avoid outliers in your measurements.
 
 General program usage tutorial
 
@@ -335,13 +335,9 @@ Detecting lines and extracting spectra in 3D data
 
 Creating color images
 
-* Color channels in same pixel grid::
-* Color image using linear transformation::
-* Color image using asinh transformation::
-
-Color channels in same pixel grid
-
-* Color image using linear transformation::
+* Color channels in same pixel grid::  Warping all inputs to the same pixel 
grid.
+* Color image using linear transformation::  A linear color mapping won't show 
much!
+* Color image using asinh transformation::  Optimizing the color range.
 
 Color image using linear transformation
 
@@ -2067,11 +2063,11 @@ For an explanation of the conventions we use in the 
example codes through the bo
 * Building the extended PSF::   How to extract an extended PSF from science 
data.
 * Sufi simulates a detection::  Simulating a detection.
 * Detecting lines and extracting spectra in 3D data::  Extracting spectra and 
emission line properties.
-* Creating color images::
+* Creating color images::       Color images that show faint and bright parts 
of source nicely.
 * Zero point of an image::      Estimate the zero point of an image.
 * Pointing pattern design::     Optimizing the pointings of your observations.
 * Moire pattern in stacking and its correction::  How to avoid this grid-based 
artifact.
-* Clipping outliers::  How to avoid outliers in your measurements.
+* Clipping outliers::           How to avoid outliers in your measurements.
 @end menu
 
 
@@ -8741,25 +8737,30 @@ Afterwards, you will see the optimized 
pseudo-narrow-band image radial profile a
 
 @node Creating color images, Zero point of an image, Detecting lines and 
extracting spectra in 3D data, Tutorials
 @section Creating color images
-Color images are fundamental tools to visualize astronomical datasets, 
allowing to extract valuable physical information from them.
+Color images are fundamental tools to visualize astronomical datasets, 
allowing to visualize valuable physical information within them.
 A color image is a composite representation derived from different channels, 
each corresponding to distinct filter or wavelength.
 In general, the most common combination is the Red-Green-Blue (RGB) channels.
-Because of the intrinsic nature of the sources, images use to have much more 
faint or noisy pixels than bright pixels.
-It is because the regions covered by the sky background or empty regions where 
there are no detectable sources are much more than those regions corresponding 
to the center of stars or galaxies.
-This non-homogeneous distribution presents challenges in the creation and 
visualization of color images.
+These three filters are hard-wired in your monitor and a most normal (for 
example smartphone) camera's pixels.
+For more on the concept and usage of colors, see @ref{Color}.
 
-In this tutorial, we present two methods for creating color images.
-The first method utilizes the @command{astconvertt} program, see 
@ref{ConvertType} for more information.
-This method involves generating a color image using three input images without 
any prior treatment.
+@cindex Dynamic range
+However, normal images (that you take with your smarphone during the day for 
example) have a very limited dynamic range (difference between brightest and 
fainest part of an image).
+For example in an image you take from a farm, the brightnest pixel (the sky) 
cannot be more than 255 times the faintest/darkest shadow in the image (because 
normal cameras produce unsigned 8 bit integers; containing @mymath{2^8=256} 
levels; see @ref{Numeric data types}).
 
-The second approach involves using the @command{astscript-rgb-faint-gray} 
script, which involves pre-processing steps that are done automatically.
-This script employs a technique that stretches the pixel value distributions 
to enhance their representation, thereby improving the visualization.
-More information about color images in astronomy and the 
@command{astscript-rgb-faint-gray} script can be found in Infante-Sainz et al. 
(2023, @url{TBD}).
+However, astronomical sources span a much wider dynamic range such that their 
central parts can be tens of millions of times brighter than their much larger 
outer regions.
+Therefore a simple linear scaling of astronomical data to the RGB 8-bit range 
will put most of the pixels on the darkest level and barely show anything!
+This present a major challenge in visualizing our astronomical images on a 
monitor, in print or for a projector when showing slides.
+
+In this tutorial, we present two methods for creating color images.
+The first method utilizes the basic and low-level @command{astconvertt} 
program which generates a color image using three input images.
+The second approach involves using the @command{astscript-rgb-faint-gray} 
script, which involves pre-processing steps to stretch the pixel value 
distributions and enhance their representation before calling 
@command{astconvertt}.
+The @command{astscript-rgb-faint-gray} script was first described in 
Infante-Sainz et al. (2023, @url{TBD}).
+But before either of these two, it is important to put the pixels of your 
input images in the same pixel grid; so in the sections below, we'll start with 
that.
 
 @menu
-* Color channels in same pixel grid::
-* Color image using linear transformation::
-* Color image using asinh transformation::
+* Color channels in same pixel grid::  Warping all inputs to the same pixel 
grid.
+* Color image using linear transformation::  A linear color mapping won't show 
much!
+* Color image using asinh transformation::  Optimizing the color range.
 @end menu
 
 @node Color channels in same pixel grid, Color image using linear 
transformation, Creating color images, Creating color images
@@ -8785,31 +8786,37 @@ $ for f in g r i; do \
   done
 @end example
 
-Next, we verify that all three images have the exact same number of pixels to 
ensure consistency, and then use @command{astscript-fits-view} to check if they 
are aligned:
+Let's have a look at thre three three images with the first command and get 
their number to ensure consistency, and then use @command{astscript-fits-view} 
to check if they are aligned:
 
 @example
-## Check the number of pixels along each axis of all images.
-$ astfits inputs/*.fits --keyvalue=NAXIS1,NAXIS2
 
 ## Open the images locked by image coordinates
 $ astscript-fits-view inputs/*.fits
+
+## Check the number of pixels along each axis of all images.
+$ astfits inputs/*.fits --keyvalue=NAXIS1,NAXIS2
+inputs/g.fits    2048   1489
+inputs/i.fits    2048   1489
+inputs/r.fits    2048   1489
 @end example
 
-Consider a faint and point-like source, for example the one that is located at 
RA=202.55718 and DEC=47.40583.
-Although this source occupies the same sky position (i.e., same RA and DEC) 
across all images, it manifests at different pixel positions in each image:
+From the first command, the images look like they cover the same astronomical 
object (M51), and with the second, we see that they have the same number of 
pixels.
+But this does not guarantee that the astronomical objects within the pixel 
grid cover the same positions on the sky!
+Let's open the images again, but this time asking DS9 to only show one at a 
time, and to ``blink'' between them:
 
 @example
-image    x (pix)   y (pix)
-g.fits   1767      1125
-r.fits   1765      1113
-i.fits   1767      1115
+$ astscript-fits-view inputs/*.fits \
+           --ds9extra="-single -zoom to fit -blink"
 @end example
 
-In essence, the images are not aligned on the same pixel grid because the same 
source does not share identical image coordinates across the channels.
-As a consequence, it is necessary to align the images before making the color 
image, otherwise this misalignment will generate artificial color gradients.
+If you pay attention, you will see that the objects within each image are at 
slightly different locations.
+If you don't immediately see it, try zooming in to any star within the image 
and let DS9 continue blinking.
+You will see that the star jumps a few pixels between each blink.
 
-To address this, you must align the three color channels onto a common pixel 
grid.
-We will employ the @ref{Warp} program, specifically focusing on the @ref{Align 
pixels with WCS considering distortions} feature.
+In essence, the images are not aligned on the same pixel grid because the same 
source does not share identical image coordinates across the channels.
+As a consequence, it is necessary to align the images before making the color 
image, otherwise this misalignment will generate multiply-peaked objects and 
artificial color gradients.
+To align the images to the same pixel grid, we will employ Gnuastro's 
@ref{Warp} program.
+In particular, focusing on the @ref{Align pixels with WCS considering 
distortions} feature.
 
 Let's take the middle (r band) filter as the reference to define our grid.
 With the first command below, let's align the r band filter to the celestial 
coordinates (so the M51 group's position angle doesn't depend on the 
orientation of the telescope when it took this image).
@@ -8823,24 +8830,23 @@ $ astwarp inputs/g.fits --gridfile=r.fits 
--output=g.fits
 $ astwarp inputs/i.fits --gridfile=r.fits --output=i.fits
 
 ## Open the images locked by image coordinates
-$ astscript-fits-view *.fits
+$ astscript-fits-view *.fits \
+           --ds9extra="-single -zoom to fit -blink"
 @end example
 
-Consider the same source (RA=202.55718 and DEC=47.40583) once more time, it 
now occupies identical x and y positions across all three images.
-In summary, these images are now precisely pixel-aligned.
+As the images blink between each other, zoom in to some stars and you will see 
that they no longer jump from one blink to the next!
+These images are now precisely pixel-aligned.
 We are now equipped with the essential data to proceed with the color image 
generation process.
 
-@menu
-* Color image using linear transformation::
-@end menu
-
 @node Color image using linear transformation, Color image using asinh 
transformation, Color channels in same pixel grid, Creating color images
 @subsection Color image using linear transformation
+
+Pereviously (in @ref{Color channels in same pixel grid}), we downloaded three 
SDSS filters of M51 and described how you can put them all in the same pixel 
grid.
 In this section, we will explore the process of generating color images using 
the input images without modifying the pixel value distributions.
-Instead, we will apply a linear transformation using the @command{astconvertt} 
program to rescale pixel values from their original ranges to the range of 0 to 
255, see @ref{Colormaps for single-channel pixels} for more details.
+Instead, we will apply a linear transformation using the @command{astconvertt} 
program to rescale pixel values from their original ranges to the range of 0 to 
255, see @ref{Colormaps for single-channel pixels} for more on the linear 
mapping.
 
-Let's create a color image using the aligned SDSS images mentioned in the 
previous section.
-The order in which you provide the images matters, so ensure that you input 
them from redder to bluer:
+Let's create our first color image using the aligned SDSS images mentioned in 
the previous section.
+The order in which you provide the images matters, so ensure that you sort the 
filters from redder to bluer (iSDSS and gSDSS are respectively the reddest and 
bluest of these three filters):
 
 @example
 $ astconvertt i.fits r.fits g.fits -g1 \
@@ -8849,15 +8855,16 @@ $ astconvertt i.fits r.fits g.fits -g1 \
 
 Open the image with your PDF viewer and have a look at it.
 Do you see something?
-You might initially notice that it appears predominantly black.
-However, upon closer inspection, you will discern regions where some color is 
visible.
-These areas correspond to the brightest sources in the images.
+You will initially notice that it appears predominantly black.
+However, upon closer inspection, you will discern very tiny points where some 
color is visible.
+These points correspond to the brightest part of the brightest sources in this 
field!
 This phenomenon exemplifies the challenge discussed in a previous section 
(@ref{Creating color images}).
 Given the vast number of pixels close to the sky background level compared to 
the relatively few very bright pixels, visualizing the entire dynamic range 
simultaneously is tricky.
 
 To address this challenge, we have several options.
 First, you can selectively choose the pixel values range to be displayed in 
the color image.
-This can be accomplished using the @option{--fluxlow} and @option{--fluxhigh}, 
where pixel values below @option{--fluxlow} are mapped to the minimum value 
(displayed as black), and pixel values above @option{--fluxhigh} are mapped to 
the maximum value.
+This can be accomplished using the @option{--fluxlow} and @option{--fluxhigh} 
options of ConvertType.
+Pixel values below @option{--fluxlow} are mapped to the minimum value 
(displayed as black), and pixel values above @option{--fluxhigh} are mapped to 
the maximum value.
 The choice of these values depends on the pixel value distribution of the 
images, which you can examine using @command{aststatistics}:
 
 @example
@@ -8877,64 +8884,47 @@ $ astconvertt i.fits r.fits g.fits -g1 \
 @end example
 
 While this adjustment eliminate a few pixels, the image remains largely 
unchanged.
-This is because only a small number of pixels were rejected, and the asymmetry 
in the distribution is toward the bright side!
-Remember that there are a lot of pixels close to zero and only a few that are 
very bright.
+This is because the truncated pixels (that had a negative value) where already 
in the smallest layer of an 8-bit imiage.
+Also, the asymmetry in the distribution is toward the bright side, not 
negative!
 
-To improve the image, we can consider removing brighter pixels, typically by 
using a multiple of the standard deviation.
+To improve the PDF visualization, we can try truncating brighter pixels, 
typically by using a multiple of the standard deviation.
 However, be careful as this parameter is highly sensitive to the dataset.
-In this example, we use @option{--fluxhigh 3.0}, approximately three times the 
standard deviation of the i-band image:
+In this example, let's use @option{--fluxhigh 3.0}, approximately three times 
the standard deviation of the i-band image:
 
 @example
 $ astconvertt i.fits r.fits g.fits -g1 \
               --fluxlow 0.0 --fluxhigh 3.0 \
               --output m51-flow-fhigh.pdf
 @end example
-By opening the new color image, you will observe that some regions of the M51 
group become visible, particularly the central areas of the brightest objects.
+
+After opening the new color image, you will observe that some regions of the 
M51 group become visible, particularly the central areas.
 However, the majority of the image remains black.
 Feel free to experiment with different values for @option{--fluxhigh} to 
enhance the image and achieve your desired result.
-For instance, by using @option{--fluxlow 0.0} and @option{--fluxhigh 0.1}, you 
can create a color image in which faint regions (pixels between 0.0 - 0.1) 
become visible, but this may lead to "saturated" appearances in the bright 
areas.
+For instance, by using @option{--fluxlow 0.0} and @option{--fluxhigh 0.1}, you 
can create a color image in which faint regions (pixels between 0.0 - 0.1) 
become more visible.
+But you will notice that the bright areas now become "saturated": you don't 
see the central parts of the galaxy any more!
 
 While these images may suffice for certain scientific goals, other cases may 
require even more refined color images that display the entire dynamic range 
accurately, particularly when visualizing low surface brightness structures.
 This can be achieved through advanced techniques that manipulate the pixel 
value distribution of the images.
 
-For example, you can experiment with taking the logarithm or the square root 
of the images before creating the color image.
+For example, you can experiment with taking the logarithm or the square root 
of the images (using @ref{Arithmetic}) before creating the color image.
 These non-linear functions transform pixel values, mapping them to a new range.
 After applying such transformations, you can use the transformed images as 
inputs to @command{astconvertt} to generate color images, as explained above.
 You can consider this an interesting exercise for exploration.
 
-A convinient function for transforming the images is the inverse hyperbolic 
sinus (asinh) function.
-The creation of color images using this transformation is implemented by the 
@command{astscript-rgb-faint-gray} script explained in the next section 
@ref{Color image using asinh transformation}.
-
-If curiosity is killing you, you can create the color image using the 
original, non-aligned images to check the effect of the image alignment.
-
-@example
-$ astconvertt inputs/i.fits inputs/r.fits inputs/g.fits -g1 \
-              --fluxlow 0.0 --fluxhigh 3.0 \
-              --output m51-not-aligned.pdf
-@end example
-
-Open the image and and zoom-in to some part of the image with fewer sources.
-You will clearly see that for each object, there are three copies, one in red 
(from the reddest filter; i), one in green (from the middle filter; r), and one 
in blue (the bluest filter; g).
-This does not happen any more when using the aligned images.
-Did you see the Warning message that was printed after running the command?
-We have implemented a check in Warp to inform you when the images are not 
aligned and can produce bad (in most cases!) outputs like this.
-
-@menu
-* Color image using asinh transformation::
-@end menu
 
 @node Color image using asinh transformation,  , Color image using linear 
transformation, Creating color images
 @subsection Color image using asinh transformation
 
-In the previous sections  we have aligned three SDSS images of M51 group 
@ref{Color channels in same pixel grid}, and create color images using 
@command{astconvertt}, @ref{Color image using linear transformation}.
-In this section, we will explore the usage of the 
@command{astscript-rgb-faint-gray} script for creating color images.
+In the previous sections  we have aligned three SDSS images of M51 group 
@ref{Color channels in same pixel grid}, and create color images using the raw 
@command{astconvertt} program in @ref{Color image using linear transformation}.
+But we saw that showing the brighter and fainter parts of the galaxy in the 
image is a major challenge!
+In this section, we will explore the usage of the 
@command{astscript-rgb-faint-gray} script to address this problem.
+
 This script employs a non-linear transformation to modify the input images 
before combining them to produce the color image.
-The primary goal of this script is to perform the asinh transformation on the 
input images, which significantly enhances the visualization of the entire 
range of pixel values, as outlined by Lupton et al. (2004, 
@url{https://arxiv.org/abs/astro-ph/0312483}).
+The primary goal of this script is to perform the asinh transformation on the 
input images, which significantly reduces the dynamic range of the entire range 
of pixel values, as outlined by Lupton et al. (2004, 
@url{https://arxiv.org/abs/astro-ph/0312483}).
 See @ref{RGB faint gray image} of this manual and Infante-Sainz et al. (2023, 
@url{TBD}) for more information.
 
-
 The @command{astscript-rgb-faint-gray} script offers various options to 
fine-tune the process, allowing you to achieve the best possible color image 
quality.
-To start, it is important to provide the input images in the order of 
decreasing wavelengths, following the Red-Green-Blue sequence (in our case, 
this translates to i, r, and g .fits images).
+To start, it is important to provide the input images in the order of 
decreasing wavelengths, following the Red-Green-Blue sequence (in our case, 
this translates to i, r, and g filters).
 Let's run the script with its default options on the aligned SDSS M51 images:
 
 @example
@@ -8942,9 +8932,9 @@ $ astscript-rgb-faint-gray i.fits r.fits g.fits -g1 \
                       --output m51-default.pdf
 @end example
 
-The script will provide you with helpful tips and automatically estimated 
parameter values.
+At the end, the script will provide you with helpful tips and automatically 
estimated parameter values.
 To enhance the output, let's go through and explain these tips step by step.
-By opening the image, you will notice that it is a color image with a black 
background, and unlike when using @command{astconvertt}, the images have 
undergone modifications, making the M51 group visible.
+By opening the image, you will notice that it is a color image with a black 
background, and unlike when using @command{astconvertt}, the images have 
undergone modifications, making the M51 group and background galaxies visible.
 However, there is significant room for improvement!
 
 The first important parameter to set is the background value, or the minimum 
value to be displayed: @option{--minimum} or @option{-m}.
@@ -8955,10 +8945,8 @@ In this particular case, a minimum value of zero for all 
images is suitable, as
 $ astscript-rgb-faint-gray i.fits r.fits g.fits -g1 \
                       --minimum 0.0 --output m51-min0.pdf
 @end example
-The difference with respect to the default image is not too much given the 
homogeneity of the input images.
-The default image appears slightly more gray in the background as it attempts 
to represent the entire range of values.
-In contrast, the image generated with a minimum value of zero exhibits a 
darker background (strong black) because it is avoiding negative pixels to be 
shown.
 
+In contrast to the previous image, the new PDF (with a minimum value of zero) 
exhibits a darker background (strong black) because it is avoiding negative 
pixels to be shown.
 Next, consider the parameters @option{--qbright} and @option{--stretch}, which 
control the asinh transformation to adjust pixel value distributions.
 The estimated values are displayed at the end of the script's execution.
 Let's decrease @option{--qbright} by an order of magnitude in order to improve 
the display of the very bright regions.
@@ -8983,12 +8971,11 @@ $ astscript-rgb-faint-gray i.fits r.fits g.fits -g1 \
 @end example
 
 Have a look at the image and check that now the faint regions are clearly 
visible.
-As you may have noticed, the ratio between the estimated parameters is such 
that qbright/stretch=10.
-This ratio is an empirical value determined through extensive testing.
-In the last example, we set this ratio to be larger (qbright/stretch = 100), 
and it appears to represent faint regions more effectively.
+In this case we have set a qbright/stretch=100; but by default it is 10.
+The value of 10 for this ratio is an empirical value determined through 
extensive testing on various types of data, but feel free to change them as you 
like.
 
-In order to have a shorter acommand-line examples, in what follow we will use 
the internally estimated values for @option{--qbright} and @option{--stretch} 
parameters.
-Let's use the @option{--black} option to create a black background images.
+Let's demonstrate the @option{--black} option to create a black background 
images (not the gray ones we produced above).
+In order to have a shorter command-line examples, in what follows we will use 
the internally estimated values for @option{--qbright} and @option{--stretch} 
parameters.
 
 @example
 $ astscript-rgb-faint-gray i.fits r.fits g.fits -g1 \
@@ -8997,12 +8984,13 @@ $ astscript-rgb-faint-gray i.fits r.fits g.fits -g1 \
                       --output m51-min0-gray.pdf
 @end example
 
-Open the image and note that now the background is shown in gray!
-In contrast with the gray background image, in this black background image the 
low surface brightness features are not visible at all.
-Consequently, the gray background color scheme is particularly useful for 
visualizing low surface brightness features.
-In this mode, the very bright regions are shown in color, intermediate and 
faint regions are shown in black, and background or noisy pixels are displayed 
in gray.
-Have a look and observe the complex, diffuse, and faint structures resulting 
from the interaction of galaxies.
-These structures were entirely hidden in the linear or black background 
images, but now, by simply showing the background in gray with the 
@option{--colorval} option, they become visible.
+Open the image and note that now the background is shown in black!
+In contrast with the gray background image, in this black background image the 
fainter/smaller stars/galaxies and the low surface brightness features are not 
visible!
+Consequently, the gray background color scheme is particularly useful for 
visualizing low surface brightness features and you will rarely need to use the 
@option{--black} option.
+
+In the default gray background mode, the very bright regions are shown in 
color, intermediate and faint regions are shown in black, and background or 
noisy pixels are displayed in gray.
+Have another look at @file{m51-min0-qbright-stretch.pdf} and observe the 
complex, diffuse, and faint structures resulting from the interaction of 
galaxies; as well as all the other background galaxies and foreground stars 
that become more visible.
+These structures were entirely hidden in the linear or black background 
images, but now, by simply showing the background in gray (with the 
@option{--colorval} option, they become visible).
 
 The paramter that defines the separation between the color and black regions 
is @option{--grayval}.
 There is also another similar option that separates the black and gray 
regions, @option{--colorval}.
@@ -9019,14 +9007,14 @@ For this value of @option{--colorval}, the estimated 
@option{--grayval} value is
 Now, decrease this parameter to 70.0 to reduce the area displayed in gray, or 
alternatively, to increase the regions shown in black.
 
 @example
-$ astscript-rgb-faint-gray i.fits r.fits g.fits --hdu 1 \
+$ astscript-rgb-faint-gray i.fits r.fits g.fits -g1 \
                       --minimum 0.0 \
                       --colorval 50.0 \
                       --grayval 70.0 \
                       --output m51-min0-gray-colorval-grayval.pdf
 @end example
 
-By adjusting these two parameters, you can obtain the best results.
+By adjusting these two parameters, you can obtain an optimal result to show 
the bright and faint parts of your data within one printable image.
 The values used here are somewhat extreme to illustrate the logic of the 
procedure, but we encourage you to experiment with values close to the 
estimated by default.
 The script can provide additional information about the pixel value 
distributions used to estimate the parameters by using the 
@option{--checkparams} option.
 
@@ -9053,12 +9041,12 @@ $ astscript-rgb-faint-gray i.fits r.fits g.fits -g1 \
 @end example
 
 This results in the output color image appearing much bluer.
-Keep in mind that altering the color of images can lead to incorrect 
subsequent analyses.
+Keep in mind that altering the color of images can lead to incorrect 
subsequent analyses by the readers/viewers of this work (they will false think 
that the galaxy is blue, and not red).
 If the reduction, photometric calibration, and the images represent what you 
consider as the red, green, and blue channels, then the output color image 
should be suitable.
 However, in certain situations, the combination of channels may not have a 
traditional color interpretation.
 For instance, combining an X-ray channel with an optical filter and a 
far-infrared image can complicate the interpretation in terms of human 
understanding of color.
 But the physical interpretation remains valid as the different channels 
(colors in the output) represent different physical phenomena of astronomical 
sources.
-Use this option with caution, as it can significantly affect the output.
+Use this option with caution, as it can significantly affect the output and 
inform your readers/viewers.
 With great power there must also come great responsibility!
 
 Two additional transformations are available to modify the appearance of the 
output color image.
@@ -9079,7 +9067,7 @@ $ astscript-rgb-faint-gray i.fits r.fits g.fits -g1 \
                       --output m51-min0-gray-contrast.pdf
 @end example
 
-As you can see, the image with higher contrast exhibits improved appealing.
+As you can see, the image with higher contrast is more appealing.
 
 Another option available for transforming the image appearance is the gamma 
correction, a non-linear transformation that can be useful in specific cases.
 You can experiment with different gamma values to observe the impact on the 
resulting image.
@@ -9099,10 +9087,10 @@ $ astscript-rgb-faint-gray i.fits r.fits g.fits -g1 \
                       --output m51-min0-gray-gamahigh.pdf
 @end example
 
-This tutorial provides a comprehensive overview of the fundamental steps 
required to construct a color image from three different images using the 
@command{astscript-rgb-faint-gray} script.
+This tutorial provides a general overview of the various optionsn to construct 
a color image from three different FITS images using the 
@command{astscript-rgb-faint-gray} script.
 Keep in mind that the optimal parameters for generating the best color image 
depend on your specific goals and the quality of your input images.
 We encourage you to follow this tutorial with the provided SDSS images and 
later with your own dataset.
-See @ref{RGB faint gray image} for more information, and please consider 
citing Infante-Sainz et al. (2023, @url{TBD}) if you use this script in your 
work.
+See @ref{RGB faint gray image} for more information, and please consider 
citing Infante-Sainz et al. (2023, @url{TBD}) if you use this script in your 
work (the full Bib@TeX{} entry of this paper will be given to you with the 
@option{--cite} option).
 
 @node Zero point of an image, Pointing pattern design, Creating color images, 
Tutorials
 @section Zero point of an image