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Try to work on an L to RGB exposure ratio of 3:1 but depends on the object and goals (i.e what you are trying to achieve). Your luminance data is critical as this is where all the resolution comes from. If you’re producing an LRGB, the RGB data doesn’t need to hold exquisite resolution. In such cases, 2x2 bin your RGB. This will allow you to acquire much better S/N in a shorter time.
The more RGB data you have the easier it is to work with. If its good, it can be stretched without introducing considerable noise. Adding noisy RGB data to a smooth luminance image defeats the purpose.
Ok, will keep things brief as this is really an entirely new topic;
Sum (or add as you put it) adds up the pixel values in all images you’re combining.
Average adds up pixel values in all images (just like Sum), but then divides them by the number of images you’re combining
Median takes the medium/middle values of the pixels in all images you’re combining. Pixels that fall outside this range are rejected.
Sigma Clip/Reject/Combine is a combination of Median and Average with other techniques to detect outlier pixels. The pixel values that deviate the most from the mean will be discarded providing the deviation is greater than the number of standard deviations configured by you. The standard deviation value is sometimes known as the Sigma Factor. Based on the Sigma Factor a new mean is calculated without the discarded value and is assigned to the pixel in the combined image
These are the main four algorithms. There are variations on these introduced by tools such as RC Console in MaximDL and CCDStack etc. For example Image Plus has a Sigma Average and Sigma Median. Ultimately they all follow similar principals.
Sum and Average are not effective a reducing noise. Some would disagree. Based on experiments have conducted with noisy data, average has an advantage over sum, but marginally. Median combine works very well to suppress noise; however there is also a loss in signal when using the algorithm. To circumvent this, you need plenty of data. Median works well for combining calibration frames. Finally Sigma X (various permutations) offers the greats flexibility at suppressing noise and enhancing signal. It does take some experimenting to get the Sigma Factor right. If incorrect you’ll lose too much valuable data.
There are also other things to consider when imaging RGB in particular image normalisation. In order to accurately present each colour channel in a balanced manner, the background pixel values of each combined R, G, B channel should be similar to each other. Some tools such as MaximDL perform the normalisation automatically as part of the colour combine process. If one channel for example blue is brighter than the others due to light pollution etc, it will dominate and through the balance out. You can use pixel math to rectify this. There are many other tricks to handling these tasks.
Cheers
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