2026-10-09 APP 2.0.0-beta48 has been released !
This is mainly a bug fix release for the reported bugs in beta47. In addition it has a significant User Interface performance improvement noticable when you need to process 1000s of images in APP.
2026-10-03 APP 2.0.0-beta47 has been released !
This is a very big release... many code optimizations/rewrites, many parts of APP are much faster and more precise, especially star analysis and registration are much faster ! Big mosaics will go much faster 🙂
And we introduce a new preview filter, which includes the new Adaptive Deep Sky Stretch and a General Levels Stretch, this has been long over due. The old preview filter (the panel on the right side to stretch and preview your data) has not been updated for a couple of years and it is a critical part of any APP session.
Adaptive Deep Sky Stretch or ADSS is a much more intuitive stretch algorithm. It is also color preserving (which is a great new addition in itself ! ) which means that the highlights are no longer bleached, color is maintained from the linear data while stretching. You can simply stretch your data using the sky background level and signal width sliders which work much easier and more intuitive. You no longer need to fiddle with the old complicated sliders with zoom buttons. All the zoom buttons for the sliders are made redundant. And you can enter slider values in the slider value fields yourself. I will make a youtube video in the coming days to explain and fully introduce ADSS. Once the video is ready, it will be highlighted on the forum.
RGB Combine upgrade !. The RGB Combine Tool has been rewritten mostly. The tool is faster and easier to use and most importantly, the Luminance slider to inject a luminance channel or channels into the RGB composite is now working correctly. It no longer bleaches the RGB colors. Combined with the new ADSS preview, you will get much better and easier great composites with great color! I will also explain this in a new youtube video.
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.
