# MarkMoHR/Awesome-Image-Colorization

:books: A collection of Deep Learning based Image Colorization and Video Colorization papers.

Repository: https://github.com/MarkMoHR/Awesome-Image-Colorization
Canonical: https://ross.abutalabs.com/products/awesome-image-colorization
License Family: other
Topics: image-colorization, user-interaction, papers, graphics, computer-vision, colorization, user-guided, automatic-colorization, deep-learning, image-colorization-paper, image-colorization-papers, color-transfer, color-palette, color-strokes
Last push: 2026-08-21T06:30:17+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 98, release rhythm 35, longevity 100
- inputs: {"age_days": 2854, "days_push": 12, "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 1164, forks 111 (observed 2026-08-28T04:03:49.845067+00:00)

## What it is
A curated awesome-list of deep learning based image and video colorization papers, organized by automatic, user-guided, and video colorization categories, with links to source code and demos. It also includes surveys and software demos like DeOldify.

## Use cases
- find papers on deep learning image colorization
- research user-guided colorization methods
- find open source colorization tools like DeOldify
- survey video colorization techniques
- find colorization papers with code implementations
- explore palette-based and scribble-based colorization research

## When to choose
- you need a research survey of colorization literature
- you want papers with linked code for image or video colorization
- you are exploring automatic vs user-guided colorization approaches

## When to avoid
- you need a ready-to-use colorization application rather than a paper list
- you need a library or API to integrate colorization into your own code

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

## Member repositories
- MarkMoHR/Awesome-Image-Colorization (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:49.845067+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 2026-08-30T06:29:29.625967+00:00, confidence not recorded.
  - readme: https://github.com/MarkMoHR/Awesome-Image-Colorization (fetched 2026-08-28T04:03:49.845067+00:00, sha 910947ba4a4e)
- Data as of 2026-08-30T08:39:29.467469+00:00.