It did take a long time to have the work finished on this and it will have a major performance boost of 30-50% over 2.0.0-beta39 from calibration to integration. We extensively optimized many critical parts of APP. All has been tested to guarantee correct optimizations. Drizzle and image resampling is much faster for instance, those modules have been completely rewritten. Much less memory usage. LNC 2.0 will be released which works much better and faster than LNC in it's current state. And more, all will be added to the release notes in the coming weeks...
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.
Yesterday I processed old M 42 data with the new multi channel & integrate all functions and got some strange artefacts and combine results. See the attached pic.
I loaded all lights (approx. 30 x 15sec p. channel) with the multi channel option enabled. I used a bias, dark and a flat (all three valid for all channels). Then processed and finally stacked the images with the "integrate per channel & all option", weights "quality" and "LN MAD winsor clip", k=3, i=1. All other config default. After that I used the combine tool to merge the 4 result stacks into a final color image.
On the RGB-combined and the integrate-all stack there some strange rectangled shapes I cannot explain. Also, one star became totally red. So, what happend here?
I will have a look at the data later today, but here's my initial reaction based on the iamge that you provided:
I can see the rectangles, this must be a problem in the LN rejection algorithm which I need to fix. The rejection map will show these rectangles as well. If you use a non-LN rejection filter, all should be okay, so you can try that at least for now.
the red stat seems to be the result of the red star being huge in the Red channel, compared to tiny in the blue channel. Are you using the RGB all integration as luminance in the combined result?
I just re-processed and integrated the data with the normal winsor clip outlier rejection filter and the result is very good (see posting: https://www.astropixelprocessor.com/community/gallery/orion-nebula-3/ ). So the red star as well as the rectangles come from the LN rejection filter.
I just re-processed and integrated the data with the normal winsor clip outlier rejection filter and the result is very good (see posting: https://www.astropixelprocessor.com/community/gallery/orion-nebula-3/ ). So the red star as well as the rectangles come from the LN rejection filter.
I have located the rootcause of the problem with the LN rejection filters 😉 and have fixed this now. The LN algorithm had a incomplete formulation actually which caused it to work NOT as expected.
All details are in the release notes for APP 1.062:
IMPROVED/FIXED, Local Normalization Outlier Rejection filters, the LN rejection filters had a bug. In some cases the LN algorithm was rejecting data based on wrong local intensity levels in the data, causing data to be rejected in frames that didn't needed to be rejected. Please look at the following images, this is an extreme case, an integration of 100 H-alpha frames from 5 different photographers with different telescopes and camera's (courtesy of Dutch Astroforum.nl). Furthermore, in addition to the fix, the implementation has been more optimized to reduce memory usage and increase speed.
Integration without outlier rejection enabled and without LNC + MBB:
Integration without outlier rejection enabled and with LNC 4th degree + MBB 30%:
Integration with sigma clip outlier rejection enabled (2 x 3.0 kappa) and with LNC 4th degree + MBB 30%, with rejection map:
Showing BUG : integration in APP 1.061 with LN sigma clip outlier rejection enabled (2 x 3.0 kappa) and with LNC 4th degree + MBB 30%, with rejection map. Notice that the rejection map shows excessive rejection on some borders, even removing part of the data completely.:
Showing FIX : integration in APP 1.062 with LN sigma clip outlier rejection enabled (2 x 3.0 kappa) and with LNC 4th degree + MBB 30%, with rejection map:
Compare the Rejection Maps between sigma clipping(left) and LN sigma clipping(right) after FIX. Notice how LN sigma clipping is less influenced by the structures in the data, giving more uniform and reliable outlier rejection:
