Author Topic: Noise Reduction Challenge  (Read 20677 times)

Offline Carlos Milovic

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Re: Noise Reduction Challenge
« Reply #15 on: 2012 June 21 07:58:20 »
Did you find any advantage on the third order TGV? I have not seeing examples with it.
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Carlos Milovic F.
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Offline Philippe B.

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Re: Noise Reduction Challenge
« Reply #16 on: 2012 June 21 08:37:05 »
 :D :D :D I said I read paper, I didn't say I understanded these papers  :D :D :D :D


Normally, 3rd order should be better ?

http://math.uni-graz.at/kunisch/papers/paper.pdf

page 28 :  While the second order model tries to approximate the image based on ane functions, the third order model additionally allows for quadratic functions, which is clearly better in this case.

Offline Carlos Milovic

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Re: Noise Reduction Challenge
« Reply #17 on: 2012 June 21 09:56:31 »
That paper is one of the hardest... I found that the latest one, from K. Bredies, was much more understandable. In fact, I'm working with the latest 2 papers, and the one applied to MRI.

It seems that I overlooked those results :P Now that you pointed them out, I can't see a "clear" advantage of TGV3 over TGV2, specially if we assume that that extra order will slow down the execution time at least a 10% (based on reported differences between TV and TGV, wich is TGV1). Indeed TGV3 seems a bit smoother, but I think that the same (or better) results may be achieved with the spatially dependent data fidelity term that I'm working on.
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Carlos Milovic F.
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Offline vicent_peris

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Re: Noise Reduction Challenge
« Reply #18 on: 2012 June 21 13:52:24 »
Hi,

Your last example is impressive. It seems that the SNR is not very high... Isn't it? If so, it's far better than the solutions we have already.

BTW, actually I think there are more important facts than noise defining different astrophotography schools.

http://www.astro-photographer.org


Best regards,
Vicent.

Offline gvanhau

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Re: Noise Reduction Challenge
« Reply #19 on: 2012 August 24 21:53:41 »
Hey Carlos
My replay may be somewhat late, but
I got this result only using only GRECstoration and a inverse lightness mask


Regards
Geert
Geert Vanhauwaert

Offline Philippe B.

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Re: Noise Reduction Challenge
« Reply #20 on: 2012 November 19 01:41:21 »
Hi Carlos
Is there some news about this new algorithm ?
Best
Philippe


Offline Carlos Milovic

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Re: Noise Reduction Challenge
« Reply #21 on: 2012 December 26 12:34:46 »
Hi Philippe

Sorry, I have not noticed your reply :P
The current status of the algorithm is this:
- There is a TGV denoise process module in Juan's hands, with a few problems in the code, and waiting for a major optimization.  Once this is done, I'll start implementing deconvolution based TGV.
- I wrote a Matlab toolkit that implements a basic TGV regularization for denoising, deconvolution and compressed sensing problems (the later for Magnetic Resonance Imaging). It is not publicy avalaible, but I may share it for development purposes.
- There is also Matlab code for a spatially dependent TGV implementation. Not as nicely packed as the previous one, but it works.
- I'm implementing the SATGV algorithm in Matlab... it is a spatially dependent variation that automatically updates the data fidelity term, using a multiscale scheme. I still lack one fundamental piece of information there, and there are some bugs in the code... but this should be ready in the short term.

So, as soon as Juan finishes the 1.8 core application, and turn into the processing modules, we'll reactivate this project. I have a lot of code to translate, and new things to try.
Regards,

Carlos Milovic F.
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Offline mschuster

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Re: Noise Reduction Challenge
« Reply #22 on: 2012 December 26 15:03:41 »
Carlos, is the method noise for TGV as expected? I am wondering if the visible structure is excessive in the residuals. Apply an STF to see it better.
Thanks,
Mike

Offline Carlos Milovic

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Re: Noise Reduction Challenge
« Reply #23 on: 2012 December 26 17:43:04 »
Hi Mike

Yes, the method is working as expected. If you compare the residual, you'll find that the structures that are a bit blured are smaller or equal to the noise level. This cannot be avoided, since TGV works like diffusion between two fluids. We may control the direction where that "water flows", but there is always a trade-off between noise removal and loss of detail. I think that I may get better results with the new adaptative algorithm, which should preserve more details in the edges.
Regards,

Carlos Milovic F.
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Offline Carlos Milovic

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Re: Noise Reduction Challenge
« Reply #24 on: 2013 May 03 14:14:37 »
Good news here from the development team :)
We have a working TGVDenoise process, that is right now under testing and optimization.

First results are here, with the bigradient image, 20% and 40% guassian noise:

Regards,

Carlos Milovic F.
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Offline Alejandro Tombolini

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Re: Noise Reduction Challenge
« Reply #25 on: 2013 May 03 16:58:43 »
It looks very well! :) Congrats!!!
Saludos, Alejandro.

Offline Philippe B.

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Re: Noise Reduction Challenge
« Reply #26 on: 2013 May 04 07:46:23 »
look forward to test this new algorithm !!! great work ! thanks