hi! sure, everything that improves noise reduction will be a welcomed addition. glancing over the paper you mention, it is based on non-local means and a global minimisation/total variation step. keep in mind that for interactive usage we need to render several megapixels through all necessary modules in a very short time frame. this usually means a couple 10 milliseconds per module.
currently we run a generalised anscombe transform (gaussian and poissonian noise), which can be combined with wavelet or non-local means. the closest they have as comparison is in table II in conjunction with BM3D, which seems to be the winning combination. working on raw data seems like a good idea and i had started that years ago in some unfinished branch. one thing about raw data is also that the black point has not been subtracted yet and that you can effectively better filter gaussian noise near zero using the negative values still contained in the data. designing denoising algorithms in this space is a bit painful to implement and needs to consider the different colour filter array layouts, which is tedious and can result in slow algorithms. also the anscombe transform doesn't work very well near zero, so this branch was based on a fisz transform and wavelets. in the end the results were usually not all that different except in one or two extreme oddball images. in this context a specialised nlmeans approach may be useful (but the gaussian part of the noise is important). cheers, jo On Sun, Feb 18, 2018 at 1:43 AM, Björn Sozumschein <wilecoyote2...@gmail.com> wrote: > 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 ___________________________________________________________________________ darktable developer mailing list to unsubscribe send a mail to darktable-dev+unsubscr...@lists.darktable.org
