2.6.4 Manually setting color-black-gray regions ¶

In Color for bright regions and grayscale for faint, we created a non-linear colored image. We used the --colorval and --grayval options to specify which regions to show in gray (faintest values), black (intermediate values) and color (brightest values). We also saw that the script uses a labeled image with three possible values for each pixel to identify how that pixel should be colored.

A useful feature of this script is the possibility of providing this labeled image as an input directly. This expands the possibilities of generating color images in a more quantitative way. In this section, we’ll use this feature to use a more physically motivated criteria to select these three regions (the surface brightness in the reddest band).

First, let’s generate a surface brightness image from the R channel. That is, the value of each pixel will be in the units of surface brightness (mag/arcsec\(^2\)). To do that, we need obtain the pixel area in arcsec and use the zero point value of the image. Then, the counts-to-sb operator of astarithmetic is used. For more on the conversion of NaN surface brightness values and the value to R_sbl (which is roughly the surface brightness limit of this image), see FITS images in a publication.

$ sb_sbl=26
$ sb_zp=23.43
$ sb_img=aligned/i-jplus.fits
$ pixarea=$(astfits $sb_img --pixelareaarcsec2 --quiet)

# Compute the SB image (set NaNs to SB of 26!)
$ astarithmetic $sb_img $sb_zp $pixarea counts-to-sb set-sb \
                sb sb isblank sb $sb_sbl gt or $sb_sbl where \
                --output=sb.fits

# Have a look at the image
$ astscript-fits-view sb.fits --ds9scale=minmax \
                      --ds9extra="-invert"

Remember that because sb.fits is a surface brightness image where lower values are brighter and higher values are fainter. Let’s build the labeled image that defines the regions (regions.fits) step-by-step with the following criteria in surface brightness (SB)

\(\rm{SB}<23\)

These are the brightest pixels, we want these in color. In the regions labeled image, these should get a value of 2.

\(23<\rm{SB}<25\)

These are the intermediate pixel values, to see the fainter parts better, we want these in pure black (no change in color in this range). In the regions labeled image, these should get a value of 1.

\(\rm{SB}>25\)

These are the faintest pixel values, we want these in a gray color map (pixels with an SB of 25 will be black and as they become fainter, they will become lighter shades of gray). In the regions labeled image, these should get a value of 0.

# SB thresholds (low and high)
$ sb_faint=25
$ sb_bright=23

# Select the three ranges of pixels.
$ astarithmetic sb.fits set-sb \
                sb $sb_bright lt set-color \
                sb $sb_bright ge sb $sb_faint lt and set-black \
                color 2 u8 x black + \
                --output=regions.fits

# Check the images
$ astscript-fits-view regions.fits

We can now use this labeled image with the --regions option for obtaining the final image with the desired regions (the R, G, B and params shell variables were set previously in Color for bright regions and grayscale for faint):

$ astscript-color-faint-gray $R $G $B $params --output=m51-sb.pdf \
                             --regions=regions.fits

Open m51-sb.pdf and have a look. Do you see how the different regions (SB intervals) have been colored differently? They come from the SB levels we defined, and because it is using absolute thresholds in physical units of surface brightness, the visualization is not only a nice looking color image, but can be used in scientific analysis.

This is really interesting because now it is possible to use color images for detecting low surface brightness features at the same time they provide quantitative measurements. Of course, here we have defined this region label image just using two surface brightness values, but it is possible to define any other labeled region image that you may need for your particular purpose.