Discovered in 1834 by John Herschel, NGC 2442 [type SAB(s)] is an intermediate spiral galaxy known by a somewhat unappealing name--the "Meathook Galaxy".  Together with the nearby NGC 2443 [type SAB(s)] the pair are also known as the "Cobra and the Mouse".   That designation is why I selected the image orientation that I used.  These galaxies lie in the constellation Volans at a distance of about 50 million years.  At a visual magnitude of 11.2 and an apparent size of 5.13' x 3.72'  (for NGC 2442) these are popular targets for imagers with access to the southern sky.  NGC 2442 is very detailed with a lot of interesting features so it's a great target for larger scopes under steady skies.

This image was a struggle.  I used 300s exposures to minimize the number of overexposed stars and I got pretty good yield out of this data set.  Out of a bit over 400 subs, I had just slightly below 50% yield at a threshold of 1.9" FWHM and 0.5 Eccentricity, which is pretty good.  The struggle began with the processing.  After making some pretty significant mistakes on my last image, I was determined to step up my game with this image.  I did my usual preliminary quick processing run to evaluate colors and the quality of the data and ran into the first difficulty.   I used channel normalization to look at the colors that the camera produced without any calibration and found a nice distribution of young, hot blue stars in the outer regions with a nice yellow/orange inner region--as one might expect.  However, when I went looking through Astrobin, APODs, and other images on the net, I found that many of them (not all) showed a very yellow cast over most of the galaxy with only a few bluish regions.  That set me scrambling to figure out how to correctly calibrate the color in my data.

I tried every method I could think of and every one of them (except PPC) produced a similar result--blue arms with a creamy yellow/orange core.  Robert Gendler and Roberto Colombari's excellent APOD of NGC 2442 from Hubble data (https://apod.nasa.gov/apod/ap170817.html) suggests colors similar to what I got.  So, in the end, I used the standard approach for galaxies of using the galaxy as the while reference.  If you look around the star field, it shows a balance of both yellow and blue stars.  Given the data that I took, I just can't understand the yellow color that shows up in so many other images.  My colors for the galaxy may swim upstream, but I think the colors in this image are about right--at least for the data that my telescope recorded.  It certainly makes sense in terms of stellar distributions within the galaxy.  

The next thing I wanted was to do a better job of preserving star colors through the stretch.  My first effort using a simple histogram stretch produced the usual result with bright round, white star cores and that's  what I wanted to eliminate.  I then set about exploring five methods in PI to stretch the data.  Masked stretch produced an interesting result with very low contrast and really terrible noise.  After messing around through multiple sessions with numerous settings and post stretch processing, I finally gave up on it.  At that point, my processing efforts languished for a couple of weeks while I was busy with other things and I just couldn't dig out of my hole.  I was stuck.  Finally, while trying to clean up a bunch of open files on PI, it crashed and I lost a lot of intermediate results and a number of random process icons.

That was just the bump that I needed to start fresh.  It turned out that I had about 20 hrs of Ha data and I was curious to see if I could pull out some pure Ha to add to the LRGB data.  I processed it to discover that there is virtually zero Ha signal so that effort was a total bust!  I had a little better luck with ArcsinhStretch and after some fiddling around, I got to a usable result. The star color was so saturated that I ultimately concluded that they didn't looked as natural as I'd like. So, I wound up making a core mask for the brightest stars to decrease the core saturation a bit.  That's a first!

Because I knew that I'd want to work on the details, I create a Lum-channel out of the actual Lum-data combined with synthetic-Lum data from the color channels.  I used the integration tool to do a weighted average of both the "real" Lum with the synthetic-Lum images.  This produced a VERY clean Lum-channel to start with.  Sharpening was accomplished with masked Deconvolution along with gentle bandwidth sharpening.  Fine structure emphasis was done with light masked HistogramEqualization at a small image scale.  Masked MLT was used to deal with noise in the faint regions.  After four full runs at this data set, I finally arrived at a point where I'm reasonably happy with the result.  Feel free to let me know what you think.  BTW, I probably could have binned the final result 2x2 to reduce the size of the image but I left it alone.  Yes, you can zoom in to a ridiculous level but that's wasn't my intent.  Just know that it might take a few seconds to load the full image view because it's a pretty big file.

Enjoy...

John