|
OK Jase, I understand, different algorithims depend on the number of subs processed, some suit minimum data, some rely on more. And I know sum is the worst on noisy data. I only ment that with carefull noise reduction beforehand, I couldnt see any reason for not using sum, given from my understanding, it best for noisless data. I havent tried all the 3rd sigma combines you mention, but I have found with the ones I have tried, the differential results were marginal, and the end result differences were quickly buried in subsequent processing. Ofcourse I also understand the need for processing depends on the quality of the raw data and combining method used. This process comes with experience, the better the earlier processes, the less time and work required in PS ;-)
|