ADAPTIVE LOW NOISE B&W CONVERSION
Curiously I was recently thinking of how a B&W conversion algorithm could work focusing on improving output SNR
. It is well known that:
1. SNR gets higher the higher RAW values are encoded, no matter the RAW channel.
2. Linear combination of 2 or more (in this case 3) signals can be optimally weighted: Y=k1*R+k2*G+k3*B (k1+k2+k3=1) in order to maximize SNR on Y. I once did the calculations once for the bi-dimensional case
assuming noise adding in quadrature: N^2 = P1^2 + P2^2
each colour plot represents the extra exposure of the higher RAW value
p is the mixing factor for the higher RAW value, 1-p for the lower RAW value
The red line represents p values for optimal mix (max. SNR) of the two differently exposed RAW values
Blue is the simple average of the two RAW values
Green is taking the highest RAW value
Y-axis represents the % of noise transferred to the final pixel with respect to the noise in the noisiest input pixel
It is easy to see that for an extra exposure above +1 +1/3, the contribution of the least exposed shot becomes negligible and we're better served just by picking the pixels of the higher exposure RAW file. The consequence for this in HDR imaging, is that if we shoot 2EV apart the fusion software should not even consider any progressive blending; otherwise it would lose sharpness and gain nothing.
A simple algorithm could:
1. Create first 3 intensity maps by deblurring the R, G and B individual channels
2. On each pixel, use the 3 intensities calculated in the previous step to calculate the optimal k1, k2, k3 set to optimise SNR on Y for that pixel
Since RAW data gets usually higher values in the G channel, its contribution to the output Y would generally prevail to maximize final SNR. It would be interesting to compare B&W valid textures obtained in this way vs a conventional B&W conversion method, specially in the deep shadows (noisiest areas that could benefit more from the SNR optimisation).
Another story is how the final image would look like in terms of colour to greyscale translation, since the contribution of all 3 input channels would softly but surely vary along all the image.ADAPTIVE HIGH LOCAL CONTRAST B&W CONVERSION
The same scheme could be used in this case to maximize local contrast
(i.e. texture detail). Specially if noise is not an issue, the Y channel mix could weight more the channel with the higher local contrast for every area in the image. So if some spectral band gets more variations in some part of the scene, the corresponding channel would prevail over flatter channels.
A mixed low noise/high local contrast scheme could be implemented as well. The first approach could help to differentiate noise from texture.
Jim I miss some sample images from your tests to make this thread more visual and funny