Quote:
Originally Posted by RickS
I ran a test on 40 x 600 sec uncalibrated Luminance images (NGC 7424 @ 2760mm focal length).
CCDStack: registered with CCDIS/High Precision, interpolated with Lanczos 36, followed by a Mean stack, no normalization, no rejection.
PI: Star alignment with default interpolation (Lanczos 3) followed by integration with no normalization or rejection.
Maxim: Auto star match registration followed by Average combine.
CCDInspector FWHM for integrated result:
PI: 2.71 arcsec
CCDStack: 2.72 arcsec
Maxim: 2.86 arcsec
Looks like a tie between CCDStack and PixInsight with Maxim in second place.
I also tried to do a registration with RegiStar but couldn't get it to work. I have used it successfully before so I'll try again later and see if I can figure out what I'm doing wrong.
Cheers,
Rick.
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I still can't get RegiStar to work
I went a bit further analysing the star shapes in my original integrations by doing a PSF estimate for the same ten stars in each. PI was consistently better than CCDStack on all ten stars but only by 2-3%. Maxim was consistently worse by 14-15%.
I also did a full calibration and integration run on the same data with PI and CCDStack. Using PI, I followed my normal procedure of tweaking the rejection algorithm and parameters to get as close to maximum S/N (determined by an integration with no rejection) while checking that the rejection was adequate by visual inspection. Linear Fit gave the best result as expected for a large number of subs. In CCDStack I used the STD Sigma-reject algorithm and tweaked parameters to get similar rejection percentages to PI. A noise estimate on the two integrations showed very little difference between them (~1% advantage to PI). Visually, there was no clear difference either.
So far there's no significant advantage to either package. If I get a chance I might try playing around with some smaller/poorer data sets.
For me there is one significant benefit from using PI. I get better calibration using the overscan region in my camera to counter bias drift. AFAIK, PI is the only amateur package that supports this. For the purpose of the comparison above I didn't use overscan.
Cheers,
Rick.