Jan 31 2026: APP 2.0.0-beta40 will soon be released with many fixes especially for Linux, upgraded development platform and 10-20% performance boost !
Jan 15 2026: FIXED LICENSE SERVER VPN Tunnel issue
 The issue could have prevented APP to start when using a VPN tunnel or another complicated network configuration, like using APP on a remote computer on a different continent. This issue is fixed now and APP should start normally.
Jan 04 2026: 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. Unfortunately, Mabula was struck by a severe flu virus in the past couple of weeks and thus could not work. He is getting much better now and he has resumed work to release 2.0.0 as soon as possible. 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.
Nov 28 2025:Â APP 2.0.0-beta39 has been released !
Hello,
First of all, THANK YOU so much for this new version 1.077. The pleasure of processing our images is all the more pleasant.
I am validating a procedure to obtain the best possible treatment for color images produced using a camera whose Bayer pattern is RGGB.
After having realized the "split channels" of my color images and in order to obtain the best "realize / ideal noise reduction ratio (ratNR)", I used the zero tab "Bayer CFA" where I used the following parameters and continued processing until integration:
* RGGB Pattern
* Adaptive Airy Disc Algorithm
* Force Bayer CFA
For stacking, I got the best results with "bayer drizzle 1.0" and "droplet size 2.59".
Thus, I obtained for each filter (R, G, B) three channels each having their own "ratNR".
For the combine RGB, I did some tests by considering all the channels R-c1, R-c2, R-c3, G-c1, G-c2, G-c3, B-c1, B-c2, B-c3 or by not considering only one channel per filter is the one with the best "ratNR" (eg, R-c2, G-c2, B-c1).
At this point, I wonder what is the best or the best strategies to test to take advantage as much as possible of the captures of our color images.
Is there a tutorial on this?
Tanks in advance and Have a nice day,
Max
