Most used options

A) Luminance and chrominance--Selection Tabs for each channel , options the same for each

C) Std. Dev.: Standard deviation of the low-pass filter (in pixels).. This parameter controls the size in pixels of the kernel used. The kernel size directly defines the sizes of the image structures that the low-pass filter will tend to remove. For example setting of 1 to 2 are used to remove small scale noise ( a setting used a lot ) and larger scales 4 to 6 can be used on low signal areas such as the background The Std Dev is what you will mainly use for setting the aggressiveness of the process

D) Amount-- Sets the implementation of the tool , setting of 0.5 to 1 are often used can be left default for a lot of the time

E) Iterations: This is the number of times that the low-pass filter is applied. The ACDNR filter is much more efficient when applied iterativly common settings of 3 to 5 , can be left default for a lot of the time

Q) Check to apply luminance mask that you will create with the bottom part of the tool, This is something you will use virtually all the time as with the use of a mask you control where you want the noise reduction

The above settings are what you will use most of the time, these are what adjust and control the low pass filter

Next I will have a look at the luminance Mask settings as these are used a lot

Edge Protection

O) Preview --Check to enable preview of luminance mask ( Also click on real time preview on bottom bar to activate )

P) Midtones / shadows / highlights --Adjust these to create your luminance mask, remember the darker = more protection and white= no protection

The remaining settings you can class as advanced and can be left default for 99% of the time

G) Robustness: When ACDNR's edge protection has to operate in presence of strong small-scale noise, it may have a hard time defining accurate edges of significant structures. For example, isolated noisy pixels can be very bright or dark, and their contributions to the definition of protected edges can be relevant. Robustness refers here to the ability of ACDNR to become immune to small-scale noise when discriminating significant image structures. Two robustness enforcing methods have been implemented: weighted average and morphological median. In both methods, for each pixel a neighborhood is defined and a robust reference value is calculated from the neighbor pixels, which is then used to command the edge protection device. Both methods have their strong points. The method based on the morphological median is especially good to preserve sharp edges. On the other hand, the weighted average method can yield more natural-looking images. You can try both of them and see which is best for your specific needs, according to your preferences.

H) Structure size: Minimum structure size used

I) Symmetry: Use the same setting for both the dark side ( No darth vader is not here ) and bright side edge protection .suggest leave as default ( checked )

J) Enable checkbox for bright sides--Leave this on

K) Threshold-- This parameter defines the relative brightness difference that triggers the edge protection mechanism. Low numbers offer higher protection--suggest leave as default

L) Overdrive-- This parameter controls the strength of edge protection. When overdrive is zero (its default value), edge protection just tries to preserve the existing pixel values of protected edges. Larger numbers is more aggressive -- suggest leave as default

M) Star protection--Check box , leave checked

N) Star threshold: As part of the bright sides edge protection parameters, Star threshold allows us to define a star edge -- suggest leave as default

Lu