Noise Reduction Challenge

[Carlos Milovic]

Did you find any advantage on the third order TGV? I have not seeing examples with it.

[Philippe B.]

  I said I read paper, I didn't say I understanded these papers 


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.

[Carlos Milovic]

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 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.

[vicent_peris]

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.

[gvanhau]

Hey Carlos
My replay may be somewhat late, but
I got this result only using only GRECstoration and a inverse lightness mask


Regards
Geert

[Philippe B.]

Hi Carlos
Is there some news about this new algorithm ?
Best
Philippe

[Carlos Milovic]

Hi Philippe

Sorry, I have not noticed your reply
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.

[mschuster]

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

[Carlos Milovic]

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.

[Carlos Milovic]

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:

[Alejandro Tombolini]

It looks very well! Congrats!!!
Saludos, Alejandro.

[Philippe B.]

look forward to test this new algorithm !!! great work ! thanks