As usual, the sky is so nice and clear once the moon is out. I was doing a little EAA and decided to take some data on the moon. While messing around with planetary imaging this summer, I had discovered the RestorationFilter within PixInsight. It works wonders, but the tricky part was trying to guess the point spread function manually for images. This time, I tried a new idea that actually worked out well.
After I took 500 frames of the moon, I slewed to a nearby bright star. With about the same exposure, I took 1000 frames on this star. I then registered and stacked the star images in Siril. After stacking the best 10% of the moon data in Autostakkert, I opened it all up in PixInsight.
First, I decomposed the color star image into its RGB components. The star was a blue color, so the blue channel was strongest. I used the blue channel to create a Moffat 2.5 point spread function of the star.
This models the way the atmosphere spreads the light from a point source. If the atmosphere wasn’t there, a star would be a perfect single point of light in one pixel, save for the Airy disc. Now that I have a pretty good model of how light is spread out, we can attempt to undo the spread by doing a deconvolution.
Here is what the deconvolution was able to do:
Incredible! I will definitely use this trick with further moon images, and I can’t wait to try it on a planet.
Other processing included a little bit of small-scale wavelet enhancement, local histogram equalization, and a lot of saturation increase.