Deconvolution
a Rust image deconvolution and restoration crate
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I've been working on deconvolution, a comprehensive Rust image deconvolution and restoration library. Deconvolution implements 28 different image deconvolution/restoration methods which range from practical blur removal techniques to research-grade scientific imaging algorithms.Features:- Top-level functions use image::DynamicImage and return images- Inverse filters, Wiener, Richardson-Lucy, constrained, proximal, Krylov, MLE restoration- Blind Richardson-Lucy, blind maximum likelihood, parametric PSF estimation- Kernel2D, Kernel3D, Transfer2D, Transfer3D, Blur2D/Blur3D- Gaussian, motion, defocus, microscopy models, support utilities, PSF/OTF conversion- Edge tapering, apodization, range normalization, NSR estimation- Deterministic blur, noise, synthetic fixture generation- ndarray support for 2D image arrays and 3D volumethis project is a WIP, of course:)
HN · launched Jun 15, 2026 · 33 upvotes (top 20%) · 0 views
Niche: image processing and editing tools · 260 launches, 21 in the last 7 days ▼ -28% · Explore this niche →
Category and tags
- Horizontal
- Dev tools
- Developer
- Not AI
- Hobby / open-source project
Visit website ↗ Source listing ↗
More like this: all 260 launches in image processing and editing tools →
Could you build this?
No Implementing 28 research-grade deconvolution and image restoration algorithms from scratch in Rust requires rigorous expertise in digital signal processing, numerical linear algebra, and scientific imaging mathematics.
What it would actually take: The crate requires implementing complex numerical algorithms such as Richardson-Lucy, Wiener filtering, Total Variation regularization, and blind deconvolution solvers, backed by high-performance FFT and matrix libraries (like ndarray or rustfft). It demands deep mathematical understanding of point spread functions (PSF), inverse problem regularization, convergence optimization, and numerical stability in floating-point arithmetic. This represents specialized scientific computing and DSP domain expertise that cannot be assembled by LLM prompting alone.
Discussion
5 comments analyzed.
Competitors mentioned: oidn-rs (Intel Open Image Denoise), Python ecosystem denoisers
Concerns raised: New school denoisers unreliable, Neural methods would negate Rust performance benefits, No public denoising API
Feature requests: User control over fidelity locally and globally, Neural denoising methods
Competitors
Other products that read as similar to this one : 300 launches clear the similarity bar, closest 8 shown.
Attention rank: #59 of 301 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 224 days after the earliest competitor.
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