# subeeshvasu/Awesome-Deblurring

A curated list of resources for Image and Video Deblurring

Repository: https://github.com/subeeshvasu/Awesome-Deblurring
Canonical: https://ross.abutalabs.com/products/awesome-deblurring
License Family: other
Topics: image-deblurring, video-deblurring, image-deconvolution, stereo-deblurring, burst-deblurring, motion-blur, kernel-estimation, camera-shake, deep-learning, restoration, deblurring, defocus-deblurring, motion-deblurring, defocus-blur
Last push: 2025-06-29T07:34:16+00:00

## Health v2 (maintenance only)
Score: 45/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 29, release rhythm 35, longevity 100
- inputs: {"age_days": 2604, "days_push": 430, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2912, forks 372 (observed 2026-08-28T04:07:29.311896+00:00)

## What it is
A curated awesome-list of research papers, code, and datasets for image and video deblurring, covering both classical non-deep-learning methods and deep learning approaches. It organizes resources by task such as blind motion deblurring, defocus deblurring, stereo and burst deblurring, and benchmark datasets.

## Use cases
- find papers on image deblurring
- research motion deblurring methods
- find deblurring benchmark datasets
- compare deep learning vs classical deconvolution approaches
- find code for video deblurring
- get started with image restoration research
- find defocus deblurring resources

## When to choose
- you are surveying the deblurring research landscape
- you need a starting point to find papers, code, and datasets for blur removal
- you want to track recent deep-learning deblurring work

## When to avoid
- you need a ready-to-use deblurring tool or library rather than a resource list
- you need restoration tasks other than deblurring, like denoising or super-resolution
- you need maintained, production-quality software

## Facets
- artifact type: learning-resource
- maturity: active
- function: image-processing, computer-vision, machine-learning
- domain: computer-vision, image-processing, deep-learning, awesome-lists
- platform: cross-platform
- tags: awesome-list, deblurring, deconvolution, motion-blur, video-restoration, research-papers, datasets

## Member repositories
- subeeshvasu/Awesome-Deblurring (main) score 45

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:29.311896+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:34:14.678737+00:00, confidence not recorded.
  - readme: https://github.com/subeeshvasu/Awesome-Deblurring (fetched 2026-08-28T04:07:29.311896+00:00, sha 2d1634d84a87)
- Data as of 2026-08-30T08:39:29.467469+00:00.