This is a two-panel mosaic taken in both RGB and narrow band. I put a lot of effort into merging the panels together seamlessly which was pretty difficult given the light pollution gradients I have in my suburban backyard. After trying several methods, I came up with a process that works for me.

General outline for PixInsight:

* Remove black edges / low signal edges from each panel so that Dynamic Background Extraction works better.

* Dynamic Background Extraction

* Photometric Color Calibration (for RGB)

* Create a rough mosaic with star alignment to get some idea of what the final image will look like, to create a synthetic star field and determine the optimum crop for the involved panels

* Create a synthetic star field by solving the rough mosaic and using Script->Render->Catalog Star Generator

* Working with each channel independently register the mosaic panels to the synthetic start field using Star Alignment

* Apply the already determined crop to resized and registered panels. I found this helps when merging the panels.

* I used David Ault’s dnaLinearFit to match the intensities of the two panels. (See http://trappedphotons.com/blog/?p=994 for more information and for a great tutorial on Mosaic processing)

* Use Gradient Merge Mosaic to put the panels together. I found that order matters so if the result isn’t what you expect try a different order.

I had tried combining color channels for each panel into a color image before merging the panels into a Mosaic, but I couldn’t get satisfactory results.

For the narrow band version of this target, I tried several blends but the straight SHO / Hubble blend seemed to provide the most contrast with my data. I created a version of the SHO image with RGB stars but ended up preferring a blend of the straight SHO image with the one with rgb stars.

One thing about this subject really surprised me. If you rotate the narrow band image 180 degrees, you get something that looks to me like a space nautilus. This was my original framing. But, turning that upside down reveals a 3D cloud or wave not even hinted by the other presentation. It’s weird how our mammalian brain interprets an image differently based on rotation.