Nice work. Old skool methods at this point. You could add some neural methods but then you'd lose any performance benefits of Rust and might as well use the richer Python ecosystem.
There are some noise-handling pieces, but no public denoising API. E.g.: Wiener/unsupervised Wiener configs, NSR estimation, regularization, and simulation helpers for Gaussian/Poisson/readout noise. The crate is focused on deconvolution.
Nice work. Old skool methods at this point. You could add some neural methods but then you'd lose any performance benefits of Rust and might as well use the richer Python ecosystem.
I am a little wary of the new school denoisers.
https://news.ycombinator.com/item?id=48263398
https://news.ycombinator.com/item?id=48258915
You raise a good point. I think a good UX would be to give the user more control over fidelity; locally, and globally.
Any denoising?
https://github.com/Twinklebear/oidn-rs
There are some noise-handling pieces, but no public denoising API. E.g.: Wiener/unsupervised Wiener configs, NSR estimation, regularization, and simulation helpers for Gaussian/Poisson/readout noise. The crate is focused on deconvolution.