MAY 4 2026: APP 2.0.0-beta44 has been released !
New improved internal memory controls should now work on all computers
May 1 2026: APP 2.0.0-beta43 has been released !
Improved internal memory controls (much more stable and faster on big datasets), fixed CPU image viewer, fixed Narrowband extraction demosaic algortihms.
Apr 29 2026 APP 2.0.0-beta42 has been released !
New improved Normalization engine, Fixed random crashes in integration, fixed RGB Combine & Calibrate Star Colors, fixed Narrowband extraction algorithms, new development platform with performance gains, bug fixes in the tools, etc...
Apr 14 2026: Google Pay, Apple Pay & WeChat Pay added as payment options
Update on the 2.0.0 release & the full manual
We are getting close to the 2.0.0 stable release and the full manual. The manual will soon become available on the website and also in PDF format. Both versions will be identical and once released, will start to follow the APP release cycle and thus will stay up-to-date to the latest APP version.
Once 2.0.0 is released, the price for APP will increase. Owner's license holders will not need to pay an upgrade fee to use 2.0.0, neither do Renter's license holders.
Hi APP team,
I'm currently having a problem with rejection on a series of images. I had never encountered this concern before.
The main difference with my previous series is that this one is made of 8 images per color whereas before I had at least 12.
Could my rejection problem be due to this insufficient number of images?
I live not far from a major airport and aircraft dragging is a problem.
I have read all the tips related to rejection but I have not found any clues.
You could tell me: "Go back to taking more pictures", but the weather has decided otherwise 😣Â
For all intents and purposes, I tried it under PixInsight. Same results, the rejection is not going well.
I think the solution will be to go back to the field: 😎Â
By any chance, I make the files available on this dropbox
I had a try on your data and even with the most aggressive settings couldn't get rid of all of the trails. Judging by eye, the data are filtered to the noise level. But the tricky part is that the eye and brain are extremely good at detecting straight lines, even well below the noise level of a single pixel. But when brain-processing the information from hundreds or even thousands of pixels, the very faint residual lines still jump out at you.
In case @mabula-admin feels up for a challenge: one way I'd try to find and filter these data are with a Hough transform, which is a way of identifying straight lines in the data, and would then allow more explicit filtering. Another option could be to use the identified outliers, create a map, and perform a grow operation on it to exclude adjacent pixels from the stack as well, even though they otherwise pass the outlier rejection filter.
