This would be prior to stretching the image to a non-linear state. MultiscaleLinearTransform can be applied equally well to linear and non-linear images, which means that using it during the initial linear stages of noise reduction is an excellent idea. ACDNR is particularly useful at getting rid of these in the non-linear state so this would be an example of using more than one noise reduction process in tandem. Please keep in mind that with fairly aggressive noise reduction, your image may appear to have single pixel sized holes (black pixels left in smoothed out small scale noise). This gives rise to good noise reduction rather than aggressive noise elimination (that gives waxy looking results). Amount may also be tweaked but the general recommendation is to keep it below 1.00 to yield a nice blend between the noise reduced image and the original image. Indeed playing around with values of Threshold and Iterations will yield optimum results for your image. Applying the process produces a noise reduced image with the mask supporting where it applies the most noise reduction. Finally, for layer 4, I set Threshold to 0.500, Amount again to 0.50 and Iterations to only 1. For layer 3, I set Threshold to 1.000, Amount again to 0.50 and Iterations again to 2. Since the first layer is the one most affected by noise, our Threshold and Iterations values are the highest here.įor layer 2, I set Threshold to 2.000, Amount again to 0.50 and Iterations to 2. Iterations defines how many times the algorithm is executed over this layer. Doing this effectively produces smoother results with nicer overall noise reduction. A setting of 0.50 is in fact a blend of 50% the original image and 50% the noise reduced image. The default setting of 1 means that once you noise reduce the image, your end result will completely be the noise reduced image. Amount defines a blend between the noise reduced image and the original image.
Threshold defines the strength of the noise reduction. We will decrease our noise reduction aggressiveness as we progress to larger layers.įor this image, I select layer 1 and set Threshold to 3.000, Amount to 0.50 and Iterations to 3. Since the first layer is more dominated by noise than the second, and the second more than the third (and so forth), we will attack the first layer the most. Doing this at the very beginning of post-processing is an excellent way to enhance the SNR on your image while it is still linear. More layers can be noise reduced, of course, but noise dominates the smallest pixel scales anyway so the first four suffices.
#DENOISER 3 SMOOTH COLORS FREE#
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#DENOISER 3 SMOOTH COLORS HOW TO#
This tutorial covers how to use each of the four aforementioned noise reduction processes, with reference to both monochrome and colour images.
It is therefore best to leave some small scale noise there to produce sharpness. Attempting to eliminate noise has side-effects of giving images a waxy look and producing larger scale blobs around the background that are essentially from very smoothed out noise. The best way to achieve low noise in images is simply capturing more exposures to stack. A key point to nail in at the very beginning is that the aim is noise reduction, not noise elimination. This particular analysis is useful at seeing how well each perform. Each is capable in their own right though users may choose to use one process or another, or a combination of a few. ATrousWaveletTransform is classically excellent at noise reduction but it is in the process of being phased out to obsolete, in favour of the more capable but similar MultiscaleLinearTransform. The main ones are namely MultiscaleLinearTransform, MultiscaleMedianTransform, ACDNR and TGVDenoise. There are a number of processes capable of noise reduction in PixInsight. This is also one of PixInsight's strong suits.
Noise reduction is one of the procedures that is considered core for post-processing of images.