H@whwang,
Wow, that already looks pretty impressive ;-) !
“Is 32bit floating point the internal data format for APP? Does relaxing it to 16bit floating point allow for 1.4 GP images without significant rebuilt of APP?”
In image integration APP converts all image data formats to normalized 32bits float data in the range of 0-1. I have implemented this, so you can combine without problems data of 16bit and 32bits, which is most usefull for combining data of different sources. Light integration is done always in 32bits, unless you feed 64bits data into APP, then integration is done in 64bits.
Relaxing it to 16bit for now will not matter. The technical limit is a limit on array dimensions (can’t be larger then 2^31 elements in Java) not on the amount of memory. I will investigate in due time how to overcome this, it should be possible off course, but like I said I’ll have to do some serious rebuilding of some modules. (I first quick search inidicated that there might be s special library which I can use.. which would save me a lot of time)
1) Is it possible to make it straight in some way?
To do it artificially would mean to create a special algorithm for this purpose. I know from literature that in using cylindrical projections, constraints are used to actually solve this. Possibly this could be the solution since a cylindrical projection would suite this dataset I think.
2) There are still brightness/contrast differences between the panels.
Yes I see, as long as the individual mosaic frames aren’t perfectly corrected for any gradients I would think this is to be expected more or less. I see that you have used MBB and LNC. All possible seams are well corrected?
3) The registration is wrong in many ways.
Yes from the Registration RMS that is saw in a previous screenshot I was expecting clear errors to show in the mosaic.
The best way to solve this would mean to start from scratch with individual subs of all panels I think, but that would mean, you have to remake all panels which is a lot of work.
For now, the best thing to try to improve registration is to increase the star count in the star analysis.
Set limit stars to 5000 and lower the
detect above noise level kappa to 4.
And then run registration with
“dynamic distortion correction”
“same camera and optics” try on and off. (If on is worse then off, I strongly suspect the registration engine of PI did more worse than good for your data, so for me this would be important to know)
But probably wait for the new version first before trying. I am actually working in the registration engine now which could also help this dataset ;-)
I’ll give you a signal when you can download it.
Finally, can you send me the result which is shown in your latest screenshot, so I can get a better idea of the size of the registration errors?