I went to pick up the old commits that added ND support, as well as your new tests and pushed this. Closing.

The idea with making our new wiener2.m deal with Nd images was good. Pyton's SciPy is doing it as well, they call it scipy.signal.wiener :

• https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.signal.wiener.html
• https://github.com/scipy/scipy/blob/v0.19.0/scipy/signal/signaltools.py#L902-L950

Here is the underlying reason why the new tests for Nd images failed for me (see comment #11 and comment #14): I used the fspecial.m file from the latest (2.6.1) image release. This still canNOT deal with 3d neighborhoods:

>> pkg list
Package Name  | Version | Installation directory
--------------+---------+-----------------------
       image *|   2.6.1 | /home/hartmut/octave/image-2.6.1

>> fspecial ("average", [3 3 3])
ans =

   0.11111   0.11111   0.11111
   0.11111   0.11111   0.11111
   0.11111   0.11111   0.11111

When I switch to the latest version of fspecial.m (in the official image repo) then all the test of wiener2.m now do PASS on my machine as well. No need to change those new tests. So I was only misled by the commit message on the change of fspecial.m. This change was not only about performance, it also make Nd neighborhoods for the average filter possible.

Conclusion: Are the maybe many more places in fspecial.m, that currently do not support the creation of Nd filter matrices (and silently return a 2d matrix)?

(I'll probably come up with new tests for wiener2 with even sized neighborhoods, tomorrow.)

Thank you for the tests and comments.  I added your new tests, fixed
the docs, adjusted input check, and made imcast support class logical.

https://bitbucket.org/carandraug/octave-image/commits/8a7e8063889672a98fed99d15b73d46a1c8a245c

Could you find write some tests for what happens with even sized nhood
such as [2 3], [4 3], and [4 4]?

> Your new test fails on my linux machine. The absolute errors are on
> the order of 1e-4 (except for the 5,5,5 position in one result
> image, this is off by 0.0018.) Maybe you should add a looser
> tolerance to this new test, or is there a typo somewhere?

There is no typo.  This works on my machine.  I have seen some tests
failing because of machine precision but that was rarely for more than
1-2 orders of magnitude.  You are suggesting changing from eps to 1e-4
which is a very large difference.  I'd be curious to find out why the
big difference.

Here is a Matlab result for a 1d image:

wiener2(ones(1,5))
ans = 0.2222    0.3926    0.3926    0.3926    0.2222

This is the same result as with the current (Carne repo) version of Octave's wiener2. So, yes, they are also using a [3,3] neighborhood for 1d images.

Could you add this 1d result as a test to your wiener2 file, Carne?

The results for empty and logical inputs will come later.

Additonal note: There is still a bug in the help string of the current repo version, Carne. It now looks like this:

 -- Function File: J =  wiener2 (I)
 -- Function File: J =  wiener2 (I, NHOOD])
 -- Function File: J =  wiener2 (I, NOISE)
 -- Function File: J =  wiener2 (I, NHOOD], NOISE)
 -- Function File: [J, NOISE] = wiener2 (I, [M, N])
Apply an adaptive noise reduction filter.

I think the two lonely closing square brackets should go. And you probably want to change the last J=... line to nhood as well.

I pushed this http://hg.code.sf.net/p/octave/image/rev/7e54312e1df2 which adds your wiener2 function to the image package.

I expanded it to work for n dimensions with this https://bitbucket.org/carandraug/octave-image/commits/ac1f6b2e230e0f5f860d7c8a9ee2435497b124ed  could you take a look? Even without the reason to support nd images, this uses convn directly which will be faster.  And turns out there's no need to use padarray because convn already does the padding with zeros.

I don't think we need to check for ndims(image)==numel(neighborhood) or to postpad nhood, since convn can handle mixed number of dimensions on both arrays.  I was unsure about how to handle singleton dimensions and its effect on the default nhood.  Should a [10 10 1 4] array default to a [3 3 3 3] or a [3 3 1 3] nhood?  I went with [3 3 3 3] because that's consistent with what happens at the moment for 1d arrays, i.e., an image of size [10 1] still uses a [3 3] nhood.  Or is this wrong?  Similarly, if a user specifies a nhood with more dimensions than the image, it's their business, we shouldn't give an error.

Also, can you confirm on recent Matlab that IM indeed cannot be logical or empty for wiener2 (similar to the issue on mean2?)

I've done the proposed changes:

https://bitbucket.org/hgimpel/octave-image/commits/e58325fdbe19ae3f4db066302d64542023937d2f

Regarding to question 3: In the literature I have only seen this method applied to 1d or 2d images. But since it only relies on the noise variance (implicitly assuming additive gaussian noise), the image variance and the image mean in some simple neighborhood, it should also work fine with Nd images. The following changes would be required for this:

• skip the input test of ndims==2 for image and neighborhood
• add an input test of ndims(image)==numel(neighborhood), maybe even postpad neighborhood with 1s beforehand.
• make sure the two filtering commands works fine (currently imfilter only supports 2d images, right? Maybe switch to convn and padarray as suggested for the meantime.)
• mention it in the documentation
• add a test for it

I don't know if Matlab allows for Nd input images. But this shouldn't keep us from doing better than them.

I will try to figure out how to create new named bookmarks in tortoise-hg. Maybe next time I can use them instead of branches.

Testing new version:

>> test wiener2
PASSES 23 out of 23 tests

Looks very good.
The ball is now in Carne's hands :-)

Looks very good, passes all the tests, and even has a nice demo.

I can only remark on a few typos:

• Missing space in line 119.
• I would change "formulas" to "equations" in line 46.
• lines 43-46 should be part of the user documentation.
• Minor incompatibility in a corner case: wiener2 (I, [-5 0]) returns zeros in Matlab, and does not issue an error. I do not know if you should fix this, because it is really an error.

I recommend to include wiener2 in image package.

Thanks :-)

I've written the function "wiener2.m" for the image package.

This function applies an adaptive noise reduction filter on 2D grayscale images. It is Matlab compatible. The algorithm is taken from the book that is cited on the Mathworks help page for this function.

To make reviewing and commenting easier, I've created my first own bitbucket repository. You can find the new function there:

https://bitbucket.org/hgimpel/octave-image/commits/9af48e1f6eaa5fcafe094f6c2f67a519874ab12c

Please let me know any comments or feedback on this.