JuliaImages / JuliaImages/ImageFiltering.jl
tutorial: spatial kernel filtering with `imfilter` and convolution theorem
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- Dominant language
- Julia
- Stars
- 104
- Forks
- 52
- PR merge metrics
- No merged PRs in 30d
Description
Now that we have several ways to apply a spatial space kernel to an image:
imfilter(img, kern)- convolution theorem
ifft(fft(img) .* fft(kern))
For the convolution theorem, we have utils like fftshift/ifftshift. We also have freqkern and spacekern functions.
Although these are "obvious" for people who already know them and use them in daily life, we need one or two demos to explain them.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the imfilter, fft, fftshift, ifftshift, freqkern, and spacekern entry points mentioned in the issue. Create one or two tutorial demos that explain spatial kernel filtering and the convolution theorem, and show the relevant Julia workflows clearly enough for readers unfamiliar with them.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- computer-vision, documentation
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100