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. Â Before I release it, I will make sure that everything is working properly and then I will release it.
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)Â 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:
