Hot Pixels - with aggressive parameters can help remove outlier pixels.Lowpass - to reduce chroma noise, see denoising styles from William Ferguson and this video.Sharpen - to help recover detail lost in denoising process.It is important to adjust parameter Threshold. Grain - after applying aggressive noise reduction images often lack detail and can look cartoonish.Dithering - see denoising style from Aurelien Pierre. By adding grain we can create an illusion of more detail in the image. This article was written after some practical advise appeared in darktable-user mailing list. In particular this thread followed by another one. There is also a good thread on dpreview forums. Intention of this article is to provide some useful pointers in one place. Note that the article was initially written for Darktable 2.4. In December 2018 version 2.6 came out which contains some improvements for Denoise – profiled module. Quoting from official Darktable documentation talking about Denoise – profiled module: Make sure you watch the video by rawfiner to learn many useful tricks and techniques for denoising in this new version. This module can eliminate noise with two different core algorithms. “non-local means” is a bit better suited to tackle luma (lightness) noise “ wavelet” has its strength in eliminating chroma (color) noise. If needed you can apply two instances of this module (see Section 3.2.4, “Multiple instances”). The “non-local means” instance should be combined with blend mode “lightness” or “HSV lightness” the “wavelet” instance with blend mode “color” or “HSV color”. For more information on blend modes have a look at Section 3.2.6, “Blending operators”. I have setup in my Darktable two new presets for Denoise – profiled module as per the description above. Using presets is faster then setting things up every time. I named them step 1 - wavelets / color and step 2 - non-local means / lighten. It is worth mentioning that the order of the modules in the processing pixelpipe matters in this case, so you can experiment which order works better for you. I found it useful to add some aggressive settings for hot pixels removal too: I also found this ticket in Darktable's issue tracking system asking for a method to process these steps in parallel, without influencing each other. I have tested this method on one of my photos taken with Nikon D7100 at ISO 1600. You can download the raw file if you want to experiment yourself. Latin hypercube sampling (LHS) is a form of stratified sampling that can be applied to multiple variables.Click on any of them to get a larger cut: Original Here are small samples at 100% magnification. The method commonly used to reduce the number or runs necessary for a Monte Carlo simulation to achieve a reasonably accurate random distribution. LHS can be incorporated into an existing Monte Carlo model fairly easily, and work with variables following any analytical probability distribution. Monte-Carlo simulations provide statistical answers to problems by performing many calculations with randomized variables, and analyzing the trends in the output data. There are many resources available describing Monte-Carlo ( history, examples, software). The concept behind LHS is not overly complex. Variables are sampled using a even sampling method, and then randomly combined sets of those variables are used for one calculation of the target function. The sampling algorithm ensures that the distribution function is sampled evenly, but still with the same probability trend. Figure 1 and figure 2 demonstrate the difference between a pure random sampling and a stratified sampling of a log-normal distribution. (These figures were generated using different versions of the same software. LATIN HYPERCUBE EXCEL SOFTWAREĭifferences within the plot, such as the left axis label and the black lines, are due to ongoing development of the software application and are not related to the issue being demonstrated.)įigure 1. 500 samples were taken using the stratified sampling method described here, which generated a very smooth curve.įigure 2. A cumulative frequency plot of “recovery factor”, which was log-normally distributed with a mean of 60% and a standard deviation of 5%. To perform the stratified sampling, the cumulative probability (100%) is divided into segments, one for each iteration of the Monte Carlo simulation.
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