With Dynamic Background Extraction, we can further remove light pollution gradients and residual amp glow from our cameras in an effort to create a more flat image. The image calibration and the local normalization process should have help out significantly with these items, making dynamic background extraction easier.
NOTE: There is an Automatic Background Extraction tool. It works just like the dynamic process except you have no control of what is background and what is a celestial object. Automatic background extraction is good to get a quick and dirty look at what is hidden in the background.
Dynamic Background Extraction – Easy
The first part is to use dynamic background extraction to remove any vignetting you might have in the image. Vignetting is where the edges are darker. Because I’m using a focal reducer in my imaging setup, I oftentimes see this in many of my unprocessed images that was not effectively removed with image calibration.
To do this successfully, we will create a single background model and apply to all images. To create the model, it is best to select the filter with the most details, oftentimes Luminance, Red or Hydrogen-alpha filters.
- Select the image and make it full screen. Zooming in so the image fills the entire area.
- Launch Dynamic Background Extraction and hit RESET
My goal is to remove vignetting. Which means I need to have the generated background model focus on the perimeter of the image.
Set the following:
- Tolerance: 1
- Shadows relaxation: 10
- Smoothing factor: .25
- Default sample radius: 250
- Samples per row: 15
- Sample Color: Change this to something that is easier to see on a gray image
- Select Generate
When dynamic background extraction generates a sampling model, those samples will cover most of the image due to our tolerance/relaxation parameters. We need to increase those parameters so that the entire outside edge is selected.
- Remove any sample boxes that are internal.
- For image correction
- Correction: Division
- Discard background model
- Replace target image
- Drag the new instance icon to the PixInsight workspace to create a copy of this model.
- Close and reload the copied instance of the dynamic background extraction.
- Change correction to subtraction and execute
- Repeat this process for all images
Dynamic Background Extraction – Custom
Another approach is to create a more focused background model by only placing the points on those areas that are truly the background. This helps us not throw away actual data.
- Sample Generation:
- Sample Radius: Increase to 25-50. This will make it easier to manipulate
- Samples per row: Set to 10-15. You don’t need hundreds.
- Sample color: Change to something other than gray
- Select Generate to see what sample points we get with default parameters
We want the process to apply most sample points, so we have less editing to do. This is where we change the model parameters:
- Model Parameters
- Smoothing Factor: Increase to .5 to create a smoother backround.
- Shadows Relaxation: If the sample points are not hitting the dark areas, increase this parameter to 5 or more. Select generate again to see what effect it has
- Tolerance: Slowly increase this parameter (hitting generate after each change) to see what points we get
Once you get the points dynamically generated, you must review each one to make sure it is not on nebulosity or stars. Delete as needed.
Once satisfied, drag the triangle to the workspace to copy this model
- Target Image Correction
- Correction: Division
- Discard background model: unselected
- Replace target image: selected
Run the process.
Verify that the image looks fine. If a sample point was placed on a star, it will do strange things to the surrounding area. If this happens, either delete/move the point and rerun.
Look at the background model. We want this to be flat, one color. After one pass, we do not have that.
You will most likely need to run dynamic background extraction multiple times to achieve a smooth/flat background.
Use this process across all images.
NOTE: When applying this to other filters, some of the points might turn red, meaning they are outside of the tolerance model and will not get applied. Increase the tolerance and select “Resize All”. Do this until they turn into valid points again. DO NOT hit Generate as this will create a brand new model.
Simple vs Custom
Two different approaches. Which result looks better?
I personally think the custom approach works better as the background pieces stand out more than the simple approach. However, when dealing with galaxies, the simple approach works really well as most of the image is background. But for nebula, I use advanced.
Dynamic Background Extraction – Advanced
There are times where the standard custom method still isn’t producing a good background extraction. For example, when dealing with LRGB filters, I encounter many more issues in my images that need to be cleaned up, like the following
There are 2 main issues with this:
- There is a strange line running from left-right in the top 1/4 of the image
- There is a donut hole in the upper right and lower left. The cause of the donut hole is a scratch on my telescope’s corrector plate. Flat frame calibration does not completely remove this from the image, and it is worst with the luminance filter. This is the focus of another section on Donut Hole removal.
In order to eliminate the strange line running through the top of the image, I increase the number of samples per row. This will help take into account these abrupt changes that should not exist. However, we still need to make sure those samples do not sit on top of a star. It is easier to see the stars if we first invert the image (Image-Invert). You will have to modify the sample colors.
With the inverted image, we can easily see the strange line running across the top part of the image as well as the sample points that are sitting on top of stars. I manually added a row of sample points as close to that line as possible.
With the increase number of sample points, my new image is:
That strange line is almost gone. Reapply the dynamic background extraction multiple times until the background model is mostly flat.
With the background gradients extracted, we can see more of the object’s detail. We might also see some imperfections in the image like dust donuts. This is the time to try and correct the dust donuts.
If we didn’t do a Mure Denoise on these images, the next step is to do noise reduction to regain that smoothness in our image that we lost with the background extraction process.