Pattern Noise

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The CMV12000 sensor suffers from dynamic row noise.

That means, a scalar offset gets added to each row. The offset is not correlated between different frames, so we can't remove it using a calibration frame (dark frame or whatever).

One can observe this noise by looking at the difference between two images taken at identical settings. There are two main components that appear obvious in such a difference frame: random noise (per pixel, increases on brighter pixels) and row noise (per line).

There are two ways to deal with this noise, after performing Black Calibration:

  • use info from black reference columns to reduce dynamic row noise without guessing anything (fast, can be implemented in real-time, see Raw_preprocessing)
  • use denoising techniques to reduce the remaining row noise (the guesswork part, slow)

1 Reducing row noise from black reference columns

https://github.com/apertus-open-source-cinema/misc-tools-utilities/commit/48de47b2a544dc32bbd5a8fd7701bb44a31ea850#diff-624053a553f49c0036b4d31282e58b2fR301

2 Reducing the remaining row noise by image filtering

  1. Filter the image with a bilateral filter, with the kernel covering only the vertical axis (basically, an edge-aware vertical blur
  2. Subtract the blurred image; the residuals will reveal the row noise
  3. Take the median value from each row of the residuals image
  4. Subtract these values from each row of the original image

3 Usage

The methods discussed here are implemented in raw2dng.

  • black reference columns are used by default, as long as you use a dark frame (since this method is fast and has no side effects)
  • to reduce the remaining row noise, use --fixrn
  • if the image also suffers from column noise, use --fixpn

Tip: the algorithm for filtering row noise is also available in mlvfs, so you can use on MLV videos (recorded with Magic Lantern) as well.

4 Samples

todo