Siril: recommended workflow for Moon processing

Dear All,

I’m wondering if there is a recommended workflow for the Moon processing? What methods for stacking would work best? I read manual:

but not all of those methods are adequate for the Moon (or a planet). I read about Wavelets somewhere and used them with some improvement to the final image (I think …). But I’m still wandering in the dark and following some tutorial and see how other people doing this, but little explanation is given why this method, and not the other. It’s a bit of trial and error, but I suspect that more experienced users might have some valuable advices.

Kind regards


Kamil

Hello, Siril is not very well suited for planetary image processing. If you just want to stack wide field images of the moon it will be fine, mostly using the image pattern or Kombat registration methods documented here: https://siril.readthedocs.io/en/stable/preprocessing/registration.html#image-pattern-alignment-planetary-full-disk
If images are 8 bit, stacking with sum is best, otherwise the mean with rejection is better.

If you have close-up images of the moon, it would be better to use a multi-point alignment software to counter the atmospheric turbulence, such as PlanetarySystemStacker.

Hello Vincent,

I confirm, I had no success using KOMBAT (images were misaligned), but I had good results with Image Pattern (full disk). I’m shooting with DSLR connected to the Sky Watcher StarTraveler 102, so I’m using NEF (RAW) for processing.

I also tried PlanetarySystemStacker, but with poor results. I used Siril to convert my NEFs to SER, which is accepted by PSS, but all my efforts resulted in misalignment, no matter what I did. So, as for now, only Siril worked for me. This is for example yesterday’s Moon:

And yesterday I learned from someone on YouTube, that Sum stacking without any calibration allows for image registration and later rejection based on the Quality factor. I think I got better results, even with 15% of the total number of images, than with 100% of them, but most of poor quality (as it turned out at the end, I was not aware that most of the images were that different).

Thanks!