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Nov 6, 2015 at 14:02 comment added JenSCDC Is this related to dark frame subtraction?
Nov 23, 2011 at 14:04 comment added jman That's a really good answer. I will check out the links your provided. Thanks!
Nov 23, 2011 at 13:48 vote accept jman
Nov 2, 2011 at 16:23 comment added Flimzy Very interesting. Thanks for the informative post, and for humoring me and my questions :)
Nov 2, 2011 at 16:20 comment added Thomas Owens @Flimzy It has nothing to do with dirty or scratched lenses or blurry images. Everything occurs on the sensor-level. There are environmental factors which do cause differences in the noise pattern, which is why you need a fairly large data set to get the noise that's consistant across images. But you can have the most blurry, scratched, dirty lenses and still identify the camera, as long as the same sensors were used.
Nov 2, 2011 at 15:34 comment added Flimzy Does this mean a dirty/scratched/defective lense won't affect this process? I suppose a lense would have to be very dirty or scratched to do more than just make a photo blury in most cases, anyway, eh?
Nov 2, 2011 at 14:40 history bounty ended mattdm
Nov 2, 2011 at 13:04 comment added Thomas Owens @Flimzy I don't believe it's significant. The noise pattern is produced by the electronics that sit behind the lense, the CCD or the CMOS sensor and all of the other components that carry charges. So even if you had random lenses of various focal length, distortion, and so on, the noise pattern that exists and is captured by the sensor in the n*m pixel output image should be similar, if not the same.
Oct 31, 2011 at 20:04 comment added Flimzy How is this affected by cameras with removable lenses? If I have two cameras and two lenses, and give you 1000 shots from each camera, but the lenses are swapped back and forth randomly, how accurate will the results be? (Assume the lenses are identical models, so focal length, distortion, etc, won't be dead give-aways)
Oct 30, 2011 at 13:36 history answered Thomas Owens CC BY-SA 3.0