1. Measuring the difference
I first toyed with the idea of writing a simple Python script to do this, but I can never figure out how to install a Python distro on my Windows machine and then install Python libraries on top of it - they always seem to come with their own Python environment ...
So, I thought about Pixinsight:
Determining the to-be-corrected columns:
Zooming into the image showed the bright columns in the darks really well:
Hovering over the column shows the coordinates at the bottom. X=4633 is the one on the right, X=1285 the one on the left.
Next, I needed to measure the average level 3 columns to the left and right of these columns and of the columns itself in an image that has as little as possible detail in those columns.
Luckily my images of the Bubble Nebula that I recently took are a good fit:
|Above is the dark with the two bright columns - at the bottom is the Bubble Nebula image that shows no details there.|
I then created the other two previews:
Now, I could use the Statistics process to get the average level of these three previews:
I manually entered the "mean" values into a spreadsheet:
When I was done with one image, I opened the next one and dragged the previews from the previous to the new image (to avoid having to define them by hand over and over again):
Pretty cumbersome, but at the end I had all the values (3 readouts x 10 frames x 2 columns):
Taking the averages of all differences, I ended up with the following values:
Column X=1285 Difference=0.0003619335296 (~DN=23)
Column X=4633 Difference=0.0003021386857 (~DN=19)
Now, I needed to find an effective way to apply this.