nice! this sounds very cool! will try it as soon as i get a minute :) cheers, jo
On Sat, Feb 24, 2018 at 6:18 AM, rawfiner <rawfi...@gmail.com> wrote: > Hi ! > > I am currently trying various things to improve noise reduction in > darktable. > It seems that NLM on RAW is a very good idea ! > On my early tests it works really well. > I have made a (ugly and slow) prototype (without sensor profiling), > available here: > https://github.com/rawfiner/darktable > (branch is draft-denoise-NLM-raw, commit > 10064058d56650900cacaf8323dc86abcdd2b66f) > > To test it, you only have to activate raw denoise module. > The threshold parameter controls the force of the non local means algorithm. > The GUI is not updated yet, there are plenty of things remaining to do, and > the code is ugly and slow. > Consider activating the module only at 100% zoom level so that it doesn't > take too long to execute. > > Please also note that I modified slightly the algorithm compared to the > normal NLM: you have to use the opacity of the module to make the balance > between the original pixel value and the value computed from OTHER pixels > (excluding the original pixel) > It seems that using an opacity of 70% works quite well. > The reason why I put this is that I don't like when denoise modules give not > uniform results (ie there is some noise remaining in some parts of the > image). This is (maybe) temporary, as I may test other ways to ensure some > uniformity. > > Also note that in the current state, I do not handle the borders of the > image, as I did not want to make the code too complicated before being > confident about the interest of the approach. > Again, this is a prototype, it will evolve pretty soon ! > > Best regards, > rawfiner > > 2018-02-21 23:18 GMT+01:00 Björn Sozumschein <wilecoyote2...@gmail.com>: >> >> Hi, >> >> thank you for the information! >> I have tried out a simple approach by profiling the color filters in the >> Sensor pattern separately, and use only patches for each pixel that are >> centered around a pixel that has the same position in the repeating filter >> pattern. The results are quite interesting, as the algorithm seems to >> perform very well in flat surfaces and texture is also rendered quite >> nicely. Maybe it is worth investigating whether that may be developed into a >> more sophisticated approach. >> >> I've uploaded the code I used and a sample image to try it out here: >> https://github.com/wilecoyote2015/NLMeans_Raw_Test >> One can use profile_camera.py to profile the camera and nlm_raw_profiled >> to denoise a raw image. The code isn't elaborated and slow as hell, but it >> provides an idea of the results. It only works with Bayer sensors. >> >> Best, >> Bjoern >> >> 2018-02-18 13:33 GMT+01:00 johannes hanika <hana...@gmail.com>: >>> >>> 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 > > > > ___________________________________________________________________________ > 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
