PixInsight > Tutorials and Processing Examples

Background noise reduction

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Harry page:
Hi

I seem to be able to handle small scale noise very well in Pixinsight , but do not have a satisfactory way of doing the background without creating the dreaded lumps . :surprised:

I would be interested in other peoples approach to this  O:)

Harry

Silvercup:
Hi Harry:

Could you post an image example?

This is an example of how well ACDNR perform with the 2 step ACDNR workflow.

One for Small Scale noise, and another for Large Scale noise.

Image is a integration of 1 subexposure each channel.



Reading this tutorial is a must if you want to do ACDNR properly.

http://www.pixinsight.com/tutorials/ACDNR/en.html

Harry page:
Hi

Yes thanks for that , I was aware of that and do know in practice what to do , but Often seem to end up with the lumpy background perhapes I am stretching my images too much  :-[  something I often do ( trying to get every last bit out of the data )

Thanks for your input


Harry

zvrastil:
I'm facing the very same problem - I am not able to remove large-scale noise with satisfactory result.
With ACDNR, the result usually feels like noise was just shifted from high frequencies to lower ones.
On contrary, programs like NeatImage or NoiseNinja seems to really "remove" noise, without "washing" it from small grains to large blobs.
I even tried to implement some noise reduction algorithms in PixInsight, but up to now, without satisfactory result.

Zbynek

Juan Conejero:

--- Quote ---On contrary, programs like NeatImage or NoiseNinja seems to really "remove" noise, without "washing" it from small grains to large blobs.
--- End quote ---

Are you aware of PixInsight's GREYCstoration implementation? I honestly think GREYCstoration is fairly superior to those plugins,

By the way, I cannot stress enough the fact that David Schumperlé, the author of the GREYCstoration, kindly given me his permission to implement his algorithms as an open-source PixInsight module.

As Silvercup has shown with his nice example, ACDNR can be used iteratively to effectively remove the noise at growing scales. I agree however that the procedures involved usually require experience and significant trial-error work. But the results normally are well worth the effort.

On the other hand, we have a powerful wavelet-based noise reduction routine implemented in the ATrousWaveletTransform tool. Unlike ACDNR and GREYCstoration though, ATWT noise reduction can be applied to linear and nonlinear images. This noise reduction is very efficient for astronomical images, although it also requires a lot of fine tuning. If you upload an image, I'll be glad to make a brief example.

One of the advantages of ACDNR and ATWT noise reduction for astronomical images is that they are purely isotropic algorithms. GREYCstoration and the plugins you have cited are anisotropic regularization algorithms. This poses the risk to introduce some alterations to the morphology and distribution of image structures that, while admissible (and even sometimes desirable) in daylight images, should in general be avoided in astronomical images. Personally I only apply slight amounts of GREYCstoration in some cases at the final stages of processing, as a final refining step.

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