I'll explain the image stacking method here.
Image stacking can yield better results, you then take multiple images at lower ISO and/or expose for a shorter time. A practical way to go about this is to just take the first picture at a high ISO and expose for long enough until you see the details you want to see, but possibly with a lot of noise and a lot of hot pixels. Suppose that the 4 minutes exposure at ISO of 3200 is such a picture. Then you should be able to get the same result by stacking e.g. 64 images with 30 seconds exposure at an ISO of 400, or 32 images with 30 seconds exposure at an ISO of 800 etc., but you are better off taking more pictures than this number. The more you take the less noise you will get.
You then choose some low ISO value and exposure time such that the total number of pictures you need to take is manageable for you. You will get the best results if you take all these pictures with long exposure noise reduction, so a dark frame subtraction is then done to each individual picture.
You then align the pictures, convert the pictures to 32 bit floating point images. If you now take the average, you reduce the noise and then you can normalize the brightness to make the faint details visible. However, there may still be outliers, the dark frame subtraction process is not perfect, the hot pixels in the dark frame are not all the same as in the original picture. So, you'll still have some outliers left and averaging doesn't get rid of them very well.
A good way to get rid of the remaining hot pixels pixels is to calculate the maximum and the minimum of the pictures (defined as the picture that has as its pixels the pixelwise maximum and the minimum of the gray values of pixels of the images). If you have e.g. 20 pictures in total, then you add all these 20 up, subtract the maximum and the minimum and then divide by 18. This amounts to taking the pixelwise average of the 18 pictures that have gray values that are not the pixelwise maximum or minimum. But this requires that all the pictures have the same exposure. If this isn't the case then you must normalize all the pictures so that they do have the same exposure, this requires converting the pictures to linear colorspace.