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Deconvolution

a Rust image deconvolution and restoration crate

Details

External ID
48540396
Source
HN
Company
—
Product
Deconvolution
Website domain
github.com
Launched
June 15, 2026
Cohort
—
Upvotes
33
Upvotes percentile
0.8019125683060109
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

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:)

Enrichment

Theme
niche creative and graphics software
Vertical
—
Function
Dev tools
Audience
Developer
AI stance
Not AI
Project type
Hobby / open-source project
Normalized one-liner
rust image deconvolution library
Manually corrected
False

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 — 124 launches clear the similarity bar, closest 8 shown.

Attention rank: #32 of 125 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 224 days after the earliest competitor.

Other launches for this product