Hi all, recently I got some good (but not great) data from Jupiter and some of its moons, and it's really the first time I've looked into processing data from my 9.25" SCT after upgrading from a 6". The larger OTA gives me the possibility of greater resolution but I'm having trouble with processing the data and balancing sharpness levels to produce an image I'm happy with.
I reckon that Jupiter is probably the hardest planet to process as it has a mixture of large and fine structures on its surface. Saturn is easier, the general rules I follow are; don't make the Cassini Division too large, sharpen to the point of showing surface details but don't make the planet look like a lollipop. The ice giants don't have much structure to show and Mars is still a bit of learning journey for me at this stage.
My problem is that I don't really know what I like, but I'm quite sure of what I don't like. I also don't have enough experience with working out how far I can push data to get the best result, so I'm throwing it out to you all to see what you can come up with. So, if you have the time and would like to play with some reasonable quality data, I'd be really interested to see what others can come up with.
The link below is a folder containing images of Jupiter and its moons was taken on 20200524 20:18:43 UT, from a 3 minute video with the best 5000, 7500 and 10000 frames stacked in AS!3 (from a total of 29801) using 3x drizzle. Jupiter was at an elevation of around 56* at the time of imaging. I thought the 7500 frame stack had the best mix of sharpness and noise and used it below, you may think differently.
I've tried a number of approaches myself, and two I'm reasonably happy with (for just the planet area) are shown below, one sharpened in Registax and the other in Astra Image. The AI image used both deconvolution and wavelets, while Registax only uses wavelets. The Registax and Astra Image settings I used are also shown below, after these were applied I fixed the colour cast and levels in Photoshop with a bit of added noise reduction (and Gaussian blur in the AI image) to taste. However to my eye, the resolution in the Registax processing looks too low, while the AI image looks over sharpened. However, maybe this is just an artifact of this data set and it's not possible to do better, however I'm not experienced enough to know better.
So I'm opening it up to anyone who would like to have a play with this data and see what you can come up with. If you do have a go I'd be very interested in your process so I might be able to incorporate it into my own, or at least it would give me some tips on which way to go.