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Denoised

Original

Moana Island Scene rendered at 8 samples per pixel with Intel® OSPRay and denoised with Intel® Open Image Denoise using prefiltered albedo and normal buffers. Publicly available dataset courtesy of Walt Disney Animation Studios. 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® oneAPI 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 Intel® 64 architecture compatible CPUs and Apple Silicon, and runs on anything from laptops, to workstations, to 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 ray tracing.

Intel Open Image Denoise internally builds on top of Intel oneAPI Deep Neural Network Library (oneDNN), and automatically exploits modern instruction sets like Intel SSE4, AVX2, and AVX-512 to achieve high denoising performance. A CPU with support for at least SSE4.1 or Apple Silicon is required to run Intel Open Image Denoise.

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 openimagedenoise@googlegroups.com.

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

Version History

Changes in v1.4.3:

• Fixed hardcoded library paths in installed macOS binaries
• Disabled VTune profiling support of oneDNN kernels by default, can be enabled using CMake options if required (DNNL_ENABLE_JIT_PROFILING and DNNL_ENABLE_ITT_TASKS)
• Upgraded to oneTBB 2021.5.0 in the official binaries

Changes in v1.4.2:

• Added support for 16-bit half-precision floating-point images
• Added oidnGetBufferData and oidnGetBufferSize functions
• Fixed performance issue on x86 hybrid architecture CPUs (e.g. Alder Lake)
• Fixed build error when using OpenImageIO 2.3 or later
• Upgraded to oneTBB 2021.4.0 in the official binaries

Changes in v1.4.1:

• Fixed crash when in-place denoising images with certain unusual resolutions
• Fixed compile error when building for Apple Silicon using some unofficial builds of ISPC

Changes in v1.4.0:

• Improved fine detail preservation
• Added the cleanAux filter parameter for further improving quality when the auxiliary feature (albedo, normal) images are noise-free
• Added support for denoising auxiliary feature images, which can be used together with the new cleanAux parameter for improving quality when the auxiliary images are noisy (recommended for final frame denoising)
• Normals are expected to be in the [-1, 1] range (but still do not have to be normalized)
• Added the oidnUpdateFilterData function which must be called when the contents of an opaque data parameter bound to a filter (e.g. weights) has been changed after committing the filter
• Added the oidnRemoveFilterImage and oidnRemoveFilterData functions for removing previously set image and opaque data parameters of filters
• Reduced the overhead of oidnCommitFilter to zero in some cases (e.g. when changing already set image buffers/pointers or the inputScale parameter)
• Reduced filter memory consumption by about 35%
• Reduced total memory consumption significantly when using multiple filters that belong to the same device
• Reduced the default maximum memory consumption to 3000 MB
• Added the OIDN_FILTER_RT and OIDN_FILTER_RTLIGHTMAP CMake options for excluding the trained filter weights from the build to significantly decrease its size
• Fixed detection of static TBB builds on Windows
• Fixed compile error when using future glibc versions
• Added oidnBenchmark option for setting custom resolutions
• Upgraded to oneTBB 2021.2.0 in the official binaries

Changes in v1.3.0:

• Improved denoising quality
  • Improved sharpness of fine details / less blurriness
  • Fewer noisy artifacts
• Slightly improved performance and lowered memory consumption
• Added directional (e.g. spherical harmonics) lightmap denoising to the RTLightmap filter
• Added inputScale filter parameter which generalizes the existing (and thus now deprecated) hdrScale parameter for non-HDR images
• Added native support for Apple Silicon and the BNNS library on macOS (currently requires rebuilding from source)
• Added OIDN_NEURAL_RUNTIME CMake option for setting the neural network runtime library
• Reduced the size of the library binary
• Fixed compile error on some older macOS versions
• Upgraded release builds to use oneTBB 2021.1.1
• Removed tbbmalloc dependency
• Appended the library version to the name of the directory containing the installed CMake files
• Training:
  • Faster training performance
  • Added mixed precision training (enabled by default)
  • Added efficient data-parallel training on multiple GPUs
  • Enabled preprocessing datasets multiple times with possibly different options
  • Minor bugfixes

Changes in v1.2.4:

• Added OIDN_API_NAMESPACE CMake option that allows to put all API functions inside a user-defined namespace
• Fixed bug when TBB_USE_GLIBCXX_VERSION is defined
• Fixed compile error when using an old compiler which does not support OpenMP SIMD
• Added compatibility with oneTBB 2021
• Export only necessary symbols on Linux and macOS

Changes in v1.2.3:

• Fixed incorrect detection of AVX-512 on macOS (sometimes causing a crash)
• Fixed inconsistent performance and costly initialization for AVX-512
• Fixed JIT’ed AVX-512 kernels not showing up correctly in VTune

Changes in v1.2.2:

• Fixed unhandled exception when canceling filter execution from the progress monitor callback function

Changes in v1.2.1:

• Fixed tiling artifacts when in-place denoising (using one of the input images as the output) high-resolution (> 1080p) images
• Fixed ghosting/color bleeding artifacts in black regions when using albedo/normal buffers
• Fixed error when building as a static library (OIDN_STATIC_LIB option)
• Fixed compile error for ISPC 1.13 and later
• Fixed minor TBB detection issues
• Fixed crash on pre-SSE4 CPUs when using some recent compilers (e.g. GCC 10)
• Link C/C++ runtime library dynamically on Windows too by default
• Renamed example apps (oidnDenoise, oidnTest)
• Added benchmark app (oidnBenchmark)
• Fixed random data augmentation seeding in training
• Fixed training warning with PyTorch 1.5 and later

Changes in v1.2.0:

• Added neural network training code
• Added support for specifying user-trained models at runtime
• Slightly improved denoising quality (e.g. less ringing artifacts, less blurriness in some cases)
• Improved denoising speed by about 7-38% (mostly depending on the compiler)
• Added OIDN_STATIC_RUNTIME CMake option (for Windows only)
• Added support for OpenImageIO to the example apps (disabled by default)
• Added check for minimum supported TBB version
• Find debug versions of TBB
• Added testing

Changes in v1.1.0:

• Added RTLightmap filter optimized for lightmaps
• Added hdrScale filter parameter for manually specifying the mapping of HDR color values to luminance levels

Changes in v1.0.0:

• Improved denoising quality
  • More details preserved
  • Less artifacts (e.g. noisy spots, color bleeding with albedo/normal)
• Added maxMemoryMB filter parameter for limiting the maximum memory consumption regardless of the image resolution, potentially at the cost of lower denoising speed. This is internally implemented by denoising the image in tiles
• Significantly reduced memory consumption (but slightly lower performance) for high resolutions (> 2K) by default: limited to about 6 GB
• Added alignment and overlap filter parameters that can be queried for manual tiled denoising
• Added verbose device parameter for setting the verbosity of the console output, and disabled all console output by default
• Fixed crash for zero-sized images

Changes in v0.9.0:

• Reduced memory consumption by about 38%
• Added support for progress monitor callback functions
• Enabled fully concurrent execution when using multiple devices
• Clamp LDR input and output colors to 1
• Fixed issue where some memory allocation errors were not reported

Changes in v0.8.2:

• Fixed wrong HDR output when the input contains infinities/NaNs
• Fixed wrong output when multiple filters were executed concurrently on separate devices with AVX-512 support. Currently the filter executions are serialized as a temporary workaround, and a full fix will be included in a future release.
• Added OIDN_STATIC_LIB CMake option for building as a static library (requires CMake 3.13.0 or later)
• Fixed CMake error when adding the library with add_subdirectory() to a project

Changes in v0.8.1:

• Fixed wrong path to TBB in the generated CMake configs
• Fixed wrong rpath in the binaries
• Fixed compile error on some macOS systems
• Fixed minor compile issues with Visual Studio
• Lowered the CPU requirement to SSE4.1
• Minor example update

Changes in v0.8.0:

• Initial beta release