Dark Frame Analysis

To improve noisy video, shot at low light conditions, it is useful to measure the distribution of the noise. Therefore I recorded several minutes of video in a setup, where no light would enter the camera (see Dark-frame subtraction). A script was used to simply sum up a big amount of recorded frames. This 'noise accumulation frame' was used for further analysis.

The camera that was being used, is a consumer grade Panasonic HC-V500. Some strange effects will be unveiled further down and you might be interesting in testing if your camera has these as well. The simple script that was used to create the plots can be downloaded here (requires Scipy, Numpy, Opencv and Matplotlib).

Unfortunately there is no 1:1 correspondence of the pixels that end up in the video file and the real pixels on the CMOS sensor. It is therefore unknown if effects seen further down, are a result of the sensor or the image processing in the camera, esp. video compression. It would be favorable to take still images at the highest possible resolution in an automated way, but at least my video camera does not have this feature.

The distribution of the blue-channel in the noise accumulation frame looks like this:

The other channels (red, green) are almost indistinguishable from this. The following plot shows the distribution of red+green+blue channel:

It can be seen that a big part of the noise is approximately normal distributed. However if you look at the noise accumulation frame directly, some structure is visible, which looks a bit like the electric field of a quadrupole:

Even though there is no direct correspondence of the pixels in the video file and the pixels on the CMOS sensors, "hot pixels" are still present (Why?). These can be seen easily by looking at details of the picture above. Keep in mind that mu is around 3.61 and sigma is around 0.47, so all values above 5 should be extremely unlikely. The plots below simply show the same as the plot above subdivided into 4 parts: