We've established that the best method to reduce noise in your images is stacking. There's just no match to layering multiple exposures and taking either the average or the median of those. In the mean time, I've received tons of questions about how you actually do this with your own images and I came across this great tutorial video by no other than Ian Norman of The Lonely Speck.
If there's one nightscape shooter to follow, it's this guy. Not only does Norman capture some of the most stunning astro-landscapes, he's also very active in developing hardware that helps you create better images yourself.
Norman occasionally puts out a tutorial about night photography too. In this one, he explains how to align multiple exposures and use Median stacking to reduce noise in Adobe Photoshop.
Median stacking is a mathematical approach to finding a "correct" pixel value; just like taking the average. Where average and median differ, is how you approach the math.
Average & Median
The average is the calculated "central" value of a set of numbers. Let's say that you have a set of 5 numbers with values ranging from 8 to 100:
8, 15, 31, 84, 100
All we have to do is add up the numbers and divide them by how many numbers there are in the set. The average of the set is 47.6.
If we would assign brightness values to those numbers, you start to see how this is useful to find a pixel brightness that isn't either of those numbers, but sits nicely in between all of them.
The Median is the "middle" of a set of numbers. If we take the same set of five numbers to find the Median value, you would just have to discard the lowest two and highest two. leaving us with 31.
Now we have two very different numbers. Averaging them leads us to 47.6 while the Median is 31.
How is this relevant to photography? In a photograph you have millions of pixels with different values. Some of those values will be influenced by noise. The idea is that all pixels pass a bit of logic before they're displayed. There are tools in Photoshop to do just that, but I couldn't have explained it better than Ian Norman does in the video above.