Not sure if this is the best stack exchange but I'll give it a shot.
I am performing background subtraction to collect Electroluminescent emission (i.e image w/ emission - image w/o emission = emission). Anyway, I am using a DSLR and collecting these images in .cr2 (canon raw format). Since the emission is weak, we wan't to make sure that we have the most data available to us and that it is as unprocessed as possible so we are converting the RAW images to .pgm (pre-bayer image data).
There is an issue, in some of the background that does not emit EL, for example a cabinet with drawers, when we perform background subtraction, we can detect all of the edges. Additionally, even if I try to do subtraction on 2 background images (both have no emission), the edges can still be clearly detected where we would expect simply black areas in the image.
Would anyone have insight on whether this is due to using .pgm, etc. Any suggestions at all?
Feel free to ask more questions about the project etc. Thanks
Below is what I get subtracting 2 images which should be essentially the same. I capture the raw image .cr2, convert it to .pgm using dcraw then subtract them using python numpy arrays and output a viewable png (what you see below.). Although, we would expect a black image(i.e subtracting two same/similar images), here I can detect all of the edges which makes no sense to me. Any suggestions?