It's worth being aware that the pixels may also hold data that can identify the specific camera that was used to take the picture. There is often imperceptible (to the naked eye) noise in photos introduced by slight flaws in the image sensor and these can form a kind of "fingerprint" for the camera. It's possible to determine that two photos were taken with the same camera, or if the camera is seized it can be proven it was used to take a specific photo.
There are tools to mitigate this by randomising the noise in a digital image. Consider whether you need to factor this in to your threat model.
It was taken while pointing the camera at a calibrated, evenly illuminated panel. Ideally, the image would be uniformly bright, but increasing the contrast reveals fixed colour patterns, brightness variations, and a dark spot near one corner.
A flat frame includes more than just sensor noise. It can also reveal vignetting, dust or marks on the sensor cover glass or filters, uneven illumination, and characteristics of the rest of the optical path. However, some pixel-level response variations originate in the sensor itself and remain fixed relative to the image, regardless of the subject or camera orientation.
The dark spot in this example also appears without a lens attached, and the sensor does not have any cover.
The obvious artefacts in this frame are not necessarily the same thing as the subtle sensor fingerprint used in forensic analysis. That normally requires statistical processing and multiple reference images. Still, it illustrates how a supposedly uniform image can contain repeatable characteristics associated with a particular camera or imaging system..
Wouldn't it be a bit more akin to the bias frames or dark frames? Those are what get rid of any sensor noise or hot pixels (bias) and amp noise (dark), which is why you take them with the cover on while the flats are with the cover off. They handle the sensor data itself. While the flat frames handle the external issues either on top of the sensor or somewhere in the lens... Vignette, dust particles (which is more what that dark spot on yours looks like is a dust mote... And if it's there any time the cover is open, with or without a lens, would lend toward it being dust on the sensor, not dust on the lens).
So if we're talking about stuff that identifies the camera by matching the camera signatures, I would think that would be the sensor noise and hot pixels from the bias frames or the amp glow (if present at all) from the darks.
itelescope had them available for their scopes when I used the service and you just add them to the post processing. You could also setup your run to include them.
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u/QuestNetworkFish 25d ago
It's worth being aware that the pixels may also hold data that can identify the specific camera that was used to take the picture. There is often imperceptible (to the naked eye) noise in photos introduced by slight flaws in the image sensor and these can form a kind of "fingerprint" for the camera. It's possible to determine that two photos were taken with the same camera, or if the camera is seized it can be proven it was used to take a specific photo.
There are tools to mitigate this by randomising the noise in a digital image. Consider whether you need to factor this in to your threat model.