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Show HN: Deconvolution – a Rust image deconvolution and restoration crate

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Why This Matters

The Deconvolution Rust crate introduces advanced image restoration capabilities, enabling developers to effectively deblur and enhance images using a variety of algorithms and tools. Its support for both known and blind PSF methods, along with comprehensive utilities, makes it a significant advancement for image processing in the tech industry, benefiting applications from photography to scientific imaging.

Key Takeaways

deconvolution

Original Deconvolved

Before (left) is the motion-blurred sample; after (right) is restored using wiener_with .

Rust image deconvolution and restoration library.

Recovering images from blur depends on a point-spread function, stable frequency-domain utilities, and careful regularization. deconvolution provides known-PSF restoration, blind workflows, PSF/OTF conversion, preprocessing helpers, simulation fixtures, and ndarray APIs.

Overview

Image API : Top-level functions use image::DynamicImage and return images ready to save.

: Top-level functions use and return images ready to save. Known PSF methods : Inverse filters, Wiener, Richardson-Lucy, constrained, proximal, Krylov, and MLE-style restoration.

: Inverse filters, Wiener, Richardson-Lucy, constrained, proximal, Krylov, and MLE-style restoration. Blind methods : Blind Richardson-Lucy, blind maximum likelihood, and parametric PSF estimation.

: Blind Richardson-Lucy, blind maximum likelihood, and parametric PSF estimation. PSF and OTF types : Kernel2D , Kernel3D , Transfer2D , Transfer3D , and Blur2D / Blur3D .

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