Intel® Open Image Denoise

High-Performance Denoising Library for Ray Tracing

Denoised
Original

Evermotion 15th Anniversary Collection scene rendered with Chaos Corona and denoised with Intel® Open Image Denoise using prefiltered albedo and normal buffers. Hover over the image (or tap on it) to move the slider between the original and denoised versions.

Overview

Intel Open Image Denoise is an open source library of high-performance, high-quality denoising filters for images rendered with ray tracing. Intel Open Image Denoise is part of the Intel® Rendering Toolkit and is released under the permissive Apache 2.0 license.

The purpose of Intel Open Image Denoise is to provide an open, high-quality, efficient, and easy-to-use denoising library that allows one to significantly reduce rendering times in ray tracing based rendering applications. It filters out the Monte Carlo noise inherent to stochastic ray tracing methods like path tracing, reducing the amount of necessary samples per pixel by even multiple orders of magnitude (depending on the desired closeness to the ground truth). A simple but flexible C/C++ API ensures that the library can be easily integrated into most existing or new rendering solutions.

At the heart of the Intel Open Image Denoise library is a collection of efficient deep learning based denoising filters, which were trained to handle a wide range of samples per pixel (spp), from 1 spp to almost fully converged. Thus it is suitable for both preview and final-frame rendering. The filters can denoise images either using only the noisy color (beauty) buffer, or, to preserve as much detail as possible, can optionally utilize auxiliary feature buffers as well (e.g. albedo, normal). Such buffers are supported by most renderers as arbitrary output variables (AOVs) or can be usually implemented with little effort.

Although the library ships with a set of pre-trained filter models, it is not mandatory to use these. To optimize a filter for a specific renderer, sample count, content type, scene, etc., it is possible to train the model using the included training toolkit and user-provided image datasets.

Intel Open Image Denoise supports a wide variety of CPUs and GPUs from different vendors:

It runs on most machines ranging from laptops to workstations and compute nodes in HPC systems. It is efficient enough to be suitable not only for offline rendering, but, depending on the hardware used, also for interactive or even real-time ray tracing.

Intel Open Image Denoise exploits modern instruction sets like SSE4, AVX2, AVX-512, and NEON on CPUs, Intel® Xe Matrix Extensions (Intel® XMX) on Intel GPUs, and tensor cores on NVIDIA GPUs to achieve high denoising performance.

System Requirements

You need an Intel® 64 (with SSE4.1) or ARM64 architecture compatible CPU to run Intel Open Image Denoise, and you need a 64-bit Windows, Linux, or macOS operating system as well.

For Intel GPU support, please also install the latest Intel graphics drivers:

Using older driver versions is not supported and Intel Open Image Denoise might run with only limited capabilities, have suboptimal performance or might be unstable. Also, Resizable BAR must be enabled in the BIOS for Intel dedicated GPUs if running on Linux, and strongly recommended if running on Windows.

For NVIDIA GPU support, please also install the latest NVIDIA graphics drivers:

For AMD GPU support, please also install the latest AMD graphics drivers:

For Apple GPU support, macOS Ventura or newer is required.

Support and Contact

Intel Open Image Denoise is under active development, and though we do our best to guarantee stable release versions a certain number of bugs, as-yet-missing features, inconsistencies, or any other issues are still possible. Should you find any such issues please report them immediately via the Intel Open Image Denoise GitHub Issue Tracker (or, if you should happen to have a fix for it, you can also send us a pull request); for missing features please contact us via email at .

Join our mailing list to receive release announcements and major news regarding Intel Open Image Denoise.

Citation

If you use Intel Open Image Denoise in a research publication, please cite the project using the following BibTeX entry:

@misc{OpenImageDenoise,
  author = {Attila T. {\'A}fra},
  title  = {{Intel\textsuperscript{\textregistered} Open Image Denoise}},
  year   = {2024},
  note   = {\url{https://www.openimagedenoise.org}}
}

Version History

Changes in v2.2.0:

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