Hello all, the Paper "Adaptive regularization of the NL-means: Application to image and video denoising" by Sutour et al, 2014 provides a nice overview regarding methods to adapt the NL Means algorithm for poisson noise and introduces regularization in order to remove typical artifacts. As far as I have read from the documentation, the profiled denoise with NL-Means uses the profile to transform the image data to uniform standard deviation in order to apply NL-Means for simple gaussian noise. As stated in Sutour et al., it isn't too complicated to adapt the algorithm to other noise using the likelihood with respect to the noise model for weights and distance.
I would be interested in experimenting a bit with this, and I believe it would also be interesting to look into adaption of the NL-means for mosaiced RAW data in order to be able to deal with the noise before it's patterns get spatially correlated by demosaicing. Do you believe that this could be worth giving it a try? I must say, however, that I'm quite busy with my master thesis and work currently, so that I won't be able to do actual coding work for darktable soon, so that I'd consider this as a mid-term side project ;) Best, Bjoern ___________________________________________________________________________ darktable developer mailing list to unsubscribe send a mail to darktable-dev+unsubscr...@lists.darktable.org
