Hi,

Looking at the code of denoise profile, I noticed a few things that seems
strange to me concerning the anscombe transform.

For wavelet codes, there is a 2x multiplier on B and R channels, while it
is not the case for the anscombe transform of non local means:
  const float wb[3] = { // twice as many samples in green channel:
                        2.0f * piece->pipe->dsc.processed_maximum[0] *
d->strength * (in_scale * in_scale), piece->pipe->dsc.processed_maximum[1]
* d->strength * (in_scale * in_scale),  2.0f *
piece->pipe->dsc.processed_maximum[2] * d->strength * (in_scale * in_scale)

Why is there this multiplier? I understand from the comment that it is
related to the fact that we have 2 times more green pixels than R or B
pixels on a bayer sensor (note that this is not perfectly valid on xtrans
sensor). Yet, I do not see the link between this, and the distribution of
the poisson noise, and thus of the anscombe transform to be done.
In addition, I do not understand why this multiplier is only here in the
case of the wavelets process, and not here in the case of the nlmeans
process.

The second thing I noticed, is that the "processed_maximum" are all equal
if highlight reconstruction is activated. Basically, they are equal to the
maximum multiplier of the white balance. Thus, the anscombe transform is
the same for R and B for instance, even though one may be much more
"amplified" than the other.
If highlight reconstruction is turned off, the processed_maximum values are
equal to the white balance multipliers, so we don't get this effect.
On images were some white balance multipliers are very different, turning
off the highlight reconstruction results in a big change in the denoising
(more or less equivalent to a big reduction of the force factor).
I guess we should use piece->pipe->dsc.temperature.coeffs instead of
piece->pipe->dsc.processed_maximum in this code.

Doing this correction will allow to "copy-paste" more reliably the settings
from one image to another, even across images that have very different
white balance.
Otherwise, a setting which works well on a picture with a white balance of
(1,1,1) for instance may not work well on a picture with a white balance of
(1, 1, 2) for instance.
Though, correcting this will break backward compatibility.

What do you think about it?
Thanks! :-)
rawfiner

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