One of my hobbies is working with archival spacecraft imagery to produce pretty pictures. Very few people realize it, but a huge amount of spacecraft data is publically accessible and relatively straightforward to work with. Most of it sits in data archives only to be referenced now and then for research purposes or used as a source for secondary data products. But with a little bit of knowledge, these archives can be used to explore much of the Solar System from the comfort of your own home. And, with a little bit of work, you can see the Solar System from entirely new angles.
This wonderful hobby can be a little intimidating, though. Figuring out where to pull data, the tools to process it, and even the quirks and relevant minutia of the spacecraft collecting the data can be confusing. My experience starting out was feeling constantly overwhelmed by the stuff I needed to know, and frustrated that the information was scattered all over the web. This page is intended to help minimize the hours you spend Googling by producing relevant tutorials and curating helpful links regarding the "process" in one place.
Your first stop should be to Emily Lakdawalla's Space Image Processing Tutorials hosted at the Planetary Society. She provides very detailed descriptions of where to find data, how to open it, and the basic processes of manipulating it. I wish I had found this resource when I had first started out, because it answers a lot of the basic questions I had.
Here is a link to the tools I have in rotation.. Most of it is freeware, with the exception of Photoshop CC (which I am slowly trying to move away from). Most of the tutorials on this site are written using Photoshop, but use features that are standard in most photo editing suites. Don't feel like you have to use these tools to get good results - if you know your way around software that performs the same function, in most cases time is better spent adapting the process to that program instead of learning something entirely new.
Image processing generally relies on a handful of core techniques. These techniques are usually very simple modifications or manipulations of the data, but can be used in sequence to pull out additional details hidden in the data. Some of these techniques blur the line between amateur and professional - some of these techinques are adopted from the scientific literature; others are skills I've learned in the hobby and have transferred to my professional work.
Usually, I don't have a plan or an end-goal in mind when I choose to work on a specific imaging data set. I see an imaging data set and decide it will make for a pretty picture. However, from time to time I do take deeper dives with a specific purpose in mind, like testing newly reported discoveries with archival data. I will add write-ups of these projects here.
A lot of my processing time is spent tracking down information about spacecraft. What is the filter range of a spacecraft? Does the sensor have a quirk that's important to note during processing? Wikipedia typically has all of these answers, but because the articles are written by multiple people with different stylistic choices, that information typically ends up scattered and sometimes hard to find. The goal of Spacecraft Sourcebook is not to replace other sources of information, but to concentrate processing details into an at-a-glance guide.
Don't be intimidated by your current skill level. Many of the pictures I started on were ones that I had never seen before despite knowing a lot of the processing community. I worried if I shared the results of a bad processing job, it would become the "definitive" version of the picture. What helped was the realization that the original data would always be there, and that if someone else (including Future Me) felt like they could do the job better, they were always free to do so.
And some advice: there is always room to grow and get better. I took up this hobby in 2014, but I'm still learning or figuring out new and better ways to do things. My work is often full of flaws that only I can see - but looking for these flaws and figuring out how to fix them next time is part of the process. Always be on the lookout for things you could have done better. And finally unlike Apollo, failure is an option. Sometimes working with a data set doesn't work out. I have dozens of failed projects, many of which still sit in the back of my mind. As your working knowledge grows and matures, keep these in your back pocket - you might develop skills that make these projects possible in the future.