Blog

Imaging Details

• Workflow: LRGB
• Red: 60*600 seconds
• Green: 60*600 seconds
• Blue: 60*600 seconds
• Total Imaging Time: 30 hours
• Imaging Dates (9 nights):
  • 4/12/2023
  • 4/13/2023
  • 4/17/2023
  • 4/24/2023
  • 4/25/2023
  • 5/2/2023
  • 5/15/2023
  • 5/19/2023
  • 5/21/2023

During the processing of this image, I couldn’t figure out what was wrong the lower -right corner of the image because a few of the stars are oblong. This typically happens when my telescope isn’t polar aligned, but this normally happens in all corners. Once I finished processing, I went and added annotation for the galaxies within this image. This oblong items are not stars. They are actually galaxies.

I started off with three really good images for HA, OIII, and SII. I did my normal integration process with drizzle integration, dynamic crop, and dynamic background extraction.

I followed my normal noise reduction process, first starting with TGV Denoise. I ran this process twice for the Red, Green, and Blue frames.

The second part of noise reduction was to use multi-scale linear transformation.

By removing the stars early on, I can focus on colors and contrast of just the galaxy without affecting the stars. Using Starnet is a way to keep the stars from getting bloated due to additional processing being applied to the image. As part of this process, I had StarNet create a star mask, which I will use later to re-add the stars. I did this for all three channels (Red, Green, Blue)

With the stars removed, I will use Channel Combination to combine the channels into RGB.

I used histogram transformation to stretch the image.

Before doing anything more to the color image, I need to go through my Luminance Workflow.

Early on, I created a color image that included the stars. From that image, I extracted the luminance channel. With a luminance image, I ran through the luminance workflow, paying attention to the galaxy arms on the edges.

The dark structure process helped highlight the central bar within the galactic nucleus.

With the luminance image processed, I can integrate it with the color image with Channel Combination. I run this process five times until I get the desired result.

I used Curves Transformation to increase the overall color saturation for a second time.

I used Curves Transformation to increase the overall brightness and colors.

Now that the image is done, I run one last noise reduction with ACDNR.

Now that the image is almost complete, it is time to add the stars back into the image. I will use the star mask the StarNet created during the star removal process for the Red, Green, and Blue channels. I used linear fit before doing a simple RGB channel combination.

Once combined, Histogram Transformation permanently stretch the image, to make the stars visible. I also used Curves Transformation to significantly increase the color saturation for the stars. Because some stars appeared green or had magenta halos, I used SCNR on the normal image and on the inverted image to clean up these incorrect colors.

For the luminance image, I also had Starnet create a starmask. I simply stretch this image, which will be used as my mask to reintegrate the stars. I applied the luminance star mask to the RGB image.

Then, using Pixel Math, take the maximum values from both images and apply to the color image. Because of the star mask applied to the color image, this formula only impacts the unprotected areas from the star mask.

To finish the image, I make a few minor tweaks to the brightness and colors of the stars using Curves Transformation, keeping the star mask applied. Once done, I prepped the image for publication with resample, crop, and annotate.