Quote:
Originally Posted by Emuhead
Thanks Marc  and I do tend to follow that method normally and its been great, but for this data Im looking for a way to determine which sub aligns the most often (rotation wise) with the highest number of other frames to get the best result from the local normalisation process (so it only fills in black sections/missing data on the least amount of subs). |
Then I'd load the aligned stack in a program that can blink through the stack and look for the smallest common overlap and select the best sub this way. It's a manual process though. When you normalise it's important to pick carefully an area that always has data or you'll get false readings. Usually an area closer to the center of the field.