Hi Srinivas,

I am familiar with the BM3D algorithm and think it would be a good fit. As far as location, the code would fit best under the restoration subfolder where there are already routines for 2D and 3D non-local means. It is not likely to be trivial to implement because it will likely need some C/Cython code to have reasonable performance, but some code for neighborhood comparisons could be reused from the existing non-local means implementation. Please see the comments below on licensing terms (existing implementations I know of are not compatibly licensed so this means it would be need to be reimplemented from a description in the journal articles).

Juan: Similar to non-local means, BM3D looks at the similarity between all patches in a local neighborhood. However rather than simply computing weights based on the patch difference, similar 2D patches are grouped into a 3D stack of patches (thus the 3D in BM3D). This stack of similar patches is transformed with a sparsifying transform, the coefficients are thresholded, the transforms is inverted and then finally there is aggregation of the denoised patches. This leads to substantial improvement over non-local means.

The IPOL journal has an open source 2D implementation in C++, but it is under an incompatible LGPLv3 license so it could not be used as the basis for an implementation in scikit-image.

http://www.ipol.im/pub/art/2012/l-bm3d/

If you just need something for immediate use, there is also a Cython-based wrapper for the IPOL BM3D code here:

https://github.com/ericmjonas/pybm3d

There is also a reference implementation from the original authors in Matlab + compiled MEX (no C/C++ source code to the MEX files is provided)

http://www.cs.tut.fi/~foi/GCF-BM3D/

The IPOL paper provides a detailed overview that could be used as the basis for making a clean implementation.

- Greg

On Wed, Nov 1, 2017 at 12:25 PM, Srinivas V <srinivasv147@gmail.com> wrote:

On Wed, Nov 1, 2017 at 9:30 PM, <scikit-image-request@python.org> wrote:
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Today's Topics:

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Message: 1
Date: Wed, 1 Nov 2017 19:37:56 +0530
From: Srinivas V <srinivasv147@gmail.com>
To: scikit-image@python.org
Subject: [scikit-image] New Contributor
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Hi,
I am Srinivas I wish to contribute to this project. I am interested in
implementing the BM3D denoising. Can someone please guide me through the
process of getting started with the existing code base.
Regards,
V Srinivas
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Message: 2
Date: Thu, 2 Nov 2017 01:18:02 +1100
From: Juan Nunez-Iglesias <jni.soma@gmail.com>
To:
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<scikit-image@python.org>
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Hi Srinivas,

Thanks for reaching out on the mailing list. It?s the right first step. =)

As a second step, please read our contributing document:
http://scikit-image.org/docs/0.13.x/contribute.html

Can you send us a reference to the algorithm you?re proposing? I have never heard of it?

Thanks,

Juan.

On 2 Nov 2017, 1:08 AM +1100, Srinivas V <srinivasv147@gmail.com>, wrote:
> Hi,
> I am Srinivas I wish to contribute to this project. I am interested in implementing the BM3D denoising. Can someone please guide me through the process of getting started with the existing code base.
> Regards,
> V Srinivas
> _______________________________________________
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> https://mail.python.org/mailman/listinfo/scikit-image
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Hi,
Actually BM3D denoising is one of the requested features on the link(https://github.com/scikit-image/scikit-image/wiki/Requested-features). The links to references to the algorithm is (http://www.ipol.im/pub/art/2012/l-bm3d/). Also I wanted to know if the code I would write would go under the transform folder or some other.
Regards,
V Srinivas.

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