tongdaxu/Rethinking-Diffusion-Posterior-Sampling-From-Conditional-Score-Estimator-to-Maximizing-a-Posterior

Official Implementation for (ICLR 2025) Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior

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Experimental

This project helps researchers and developers improve image quality by taking degraded images (like blurry or low-resolution photos) and producing cleaner, higher-quality versions. It's designed for those working with diffusion models for image restoration tasks. You input an image and specify the type of degradation it has, and the system outputs a reconstructed, improved image.

No commits in the last 6 months.

Use this if you are a machine learning researcher or developer working with diffusion models and need to evaluate or implement advanced posterior sampling techniques for image super-resolution or deblurring.

Not ideal if you are a casual user looking for a simple, off-the-shelf image enhancement application without needing to engage with model training or command-line scripts.

image-restoration super-resolution deblurring diffusion-models computer-vision-research
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 8 / 25
Community 4 / 25

How are scores calculated?

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21

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Language

Python

License

Last pushed

Jan 31, 2025

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