UPDATE FROM MABULA
I have had a very rough 2 months unfortunately health wise. I was struck 3x in a row with bacterial infections. The second infection occurred after a routine hospital checkup and I became very sick. I had to rest and take a lot of antibiotics. Once I was recovering and restarting work 1 month ago, I again became sick. The infection was not yet gone, so even more antibiotics and rest was needed. Needless to say, it took a lot of my energy and I needed a lot of rest.
Finally, the infection is really gone and my energy is coming back step-by-step now and I have started work again. I am terribly sorry to have kept you waiting for support. I will address all outstanding questions and e-mails step-by step and will be on the forum daily from today.
APP 2.0.0-beta47 will come soon as well, a lot of work was already completed before I became very ill, so the release is also nearly ready for you. Beta47 will be much faster actually. Many workflows will be more than 2x faster, mosaics can even be 10x faster than before because registration really received a major boost... all compared to beta46. Before I release it, I will make sure that everything is working properly and then I will release it.
Hello everyone.
I have a question related to the settings to eliminate traces of satellites on the stack.
Do you have any experience with this? What settings are optimal? There is more and more of it and there is a growing problem with them.
Below 14 frames from last night, almost every track has a trace, sometimes even two 🙁
Just sigma clipping, that is made for this. In 1.079 these settings are set automatically, if you use 20 or more frames, you will get the best rejection.
What if I have less than 20 frames?
Sometimes, when I take supernova photos, I collect 10-15 frames. What will be the best manual settings then.
The automatic setting in APP 1.079 will already set the correct algorithm if you have less then 20 frames. Those will be working as well, just a bit less well as more data will always work better for detection of unwanted objects. If you deselect the automatic setting, you can choose the algorithms yourself and the tooltips will explain them then.
I used automatic integration and settings as on the print screen.
Either way, the satellite tracks remain. Maybe I should change something in the LNC settings? MBB?
29 light frames were used for the stack.
I have been struggling with this as well.
@vincent-mod Is the outlier removal an iterative process? It seems that it's just a one-step process in the current implementation. A severe outlier (like a satellite trail) is detected and removed, but somehow the average of all data for that pixel value is still affected a bit. Adding an extra iteration to re-determine the average once more after outlier removal should make the process more robust, at the expense of computation time.
I used automatic integration and settings as on the print screen.
Either way, the satellite tracks remain. Maybe I should change something in the LNC settings? MBB?
29 light frames were used for the stack.
Ok, interesting, I'd expect the automatic rejection to be a bit more effective, most likely when you have more data then. You can switch off auto and change to average integration. Then select "adaptive rejection" as the rejection filter and maybe set the kappa high to 2.5, you can experiment with that a bit (lower is more aggressive rejection).
The exact algorithm mechanics I don't know about (yet), working on that slowly. 😉 But like I mention above, it's likely the aggressiveness of the filter, which you can change by coming off of auto in this case.
Hi Ralph,
As a simple test, try to set the integration mode to "Median" and simply ignore the outlier rejection parameters. If you have your satellites streaks in less than half of the images then using the median should remove them entirely. The issue with using the average is that the streak is there to begin with (if for some reason it is not taken out by outlier rejection) and the other images are trying to average it down to the standard background level. With Median the streak will simply not be there at all, regardless of any outlier rejection applied.
