# RenderKit/oidn

Intel® Open Image Denoise library

Repository: https://github.com/RenderKit/oidn
Canonical: https://ross.abutalabs.com/products/oidn
Homepage: https://www.openimagedenoise.org/
Language: C++
License: Apache-2.0
License Family: permissive
Last push: 2026-08-18T16:36:30+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 86, longevity 100
- inputs: {"age_days": 2774, "days_push": 15, "days_rel": 15, "gap_med": 78.5, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2089, forks 198 (observed 2026-08-28T04:06:12.230341+00:00)

## What it is
Intel Open Image Denoise is an open-source library of high-performance, deep-learning-based denoising filters for images rendered with ray tracing. It reduces Monte Carlo noise in path-traced renders, cutting required samples per pixel by orders of magnitude, and integrates via a simple C/C++ API.

## Use cases
- denoise noisy path-traced renders with few samples per pixel
- integrate AI denoising into a custom ray tracing renderer
- use albedo and normal AOV buffers to preserve render detail
- denoise renders on CPU or GPU across Intel, NVIDIA, and AMD hardware
- train a custom denoising model for a specific renderer or scene type
- speed up final-frame rendering in production pipelines

## When to choose
- you render with ray tracing or path tracing and need to cut sample counts
- you want an award-winning, permissively licensed (Apache-2.0) denoiser
- you need CPU and multi-vendor GPU support including Apple silicon
- you want pre-trained filters plus the option to train custom models

## When to avoid
- your noise is not Monte Carlo rendering noise (e.g. photo denoising)
- you need a turnkey GUI tool rather than an embeddable library
- your platform lacks SSE4.1 or a supported GPU

## Facets
- artifact type: library
- maturity: stable
- function: image-processing, machine-learning, graphics
- domain: graphics, computer-vision, deep-learning
- platform: cpp, cross-platform, windows
- tags: denoising, ray-tracing, monte-carlo, rendering, deep-learning-filters, c-api, game-development, gpu, linux, macos

## Member repositories
- RenderKit/oidn (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:12.230341+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 2026-08-30T02:55:37.778549+00:00, confidence not recorded.
  - readme: https://github.com/RenderKit/oidn (fetched 2026-08-28T04:06:12.230341+00:00, sha a4ddf63c25e5)
  - homepage: https://www.openimagedenoise.org/ (fetched 2026-08-29T10:35:24.150320+00:00, sha af2c14c6516e)
  - site_page: https://www.openimagedenoise.org/documentation.html (fetched 2026-08-29T10:35:24.161081+00:00, sha 0856f08a537f)
- Data as of 2026-08-30T08:39:29.467469+00:00.