4. Denoising
As of version 5 the denoising option have been extended with the possibility to apply denoise filters in 2 passes. The first pass is meant for de-speckling and the 2nd pass for smoothening. Different filters can be applied in those 2 passes.
4.1 Denoise Pass 1
4.1.1 Ian's NR
This newly added denoising filter has very effective de-speckling capabilities. It will remove noise while at the same time keeping almost all details in tact. The animation on the right shows an example. This filter is from the well known open source G'MIC image processing tool. In LuckyStackWorker the filter can be controlled by 2 sliders: Amount and Recovery. By inceasing the amount a stronger denoising will be applied. The recovery is meant for recovering details that might have been wiped out by the application of the filter.
4.1.2 Sigma 1 denoising
Sigma denoising is the other algorithm supported. Sigma 1 is somewhat less effective than Ian's NR, but is a good choice for images with few or very fine noise, and is very effective at preserving details versus noise patches (outliers). In case you did not install G'MIC (only applies to non windows versions) Ian's NR will not be available. In that case sigma 1 denoising is a good alternative. The filter can be controlled using the following sliders:
1 Amount: Higher values will increase the noise reduction and is better as preserving edges but will also be less effective on noisy images.
2. Radius: Allows to select the pixel size where the filter is applied to. The larger the radius chosen the more pixels it will use to average out the noise.
3. Iterations: this is the number of times that the Sigma filter algorithm is applied.
4.2 Denoise Pass 2
This 2nd pass as said is meant to obtain a smoother and more natural outcome and the choice of the 2 filters are very effective at this. After first applying the first pass, optionally this 2nd pass can be applied.
4.2.1 Savitzky-Golay smoothening
This option is based on the well known and very effective 2D Savitzky-Golay smoothening algorithm. This algorithm is very effective in removing noise while at the same time preserving the contrast. It can be controlled with 3 sliders. The Kernel Size slider selects the algorithms kernel size. A smaller size will work better on very fine noise but is less strong than when you choose a larger kernel size. Larger kernel sizes work better on larger noise patches, but may also blur out fine details. The amount slider can be used to blend the non denoised version of the image with the fully denoised version based on the choose kernel size. Finally the number of iterations stands for the number of times that the algorithm is applied. Applying multiple iterations can result in better detail preservations and less blurring out of fine details while still applying a smaller kernel size.
4.2.2 Sigma 2 denoising
Sigma denoising is the other algorithm supported. Although it can obtain very similar results as the Savitzky-Golay algorithm, the latter is better at preserving contrast. The Sigma filter 2 algorithm uses 2 sliders. Mode 2 behaves more like a traditional averaging filter. Applying Mode 2 will result in more noise reduction but it will also soften edges. This mode works best for noisy images.
1. Radius: Allows to select the pixel size where the filter is applied to. The larger the radius chosen the more pixels it will use to average out the noise.
2. Iterations: this is the number of times that the Sigma filter algorithm is applied.