Based on https://github.com/DeclanRussell/IntelOIDenoiser.
I was fustrated that the original tool won't build proprely on Linux platforms. There is a modified version that natively build on linux, with scripts to build Intel Open Image Denoise.
Run the script OIDNDownload.sh to download a local copy of OIDN.
Then:
make
The executable will be found on the bin folder.
Finally, copy
libOpenImageDenoise.so
libOpenImageDenoise.so.1
libOpenImageDenoise.so.1.4.1
To the bin folder (If you used the script, these can be found on the oidn/build folder).
And set the LD_LIBRARY_PATH if needed.
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:.
Command line parameters
- -v [int] : log verbosity level 0:disabled 1:simple 2:full (default 2)
- -i [string] : path to input image
- -o [string] : path to output image
- -a [string] : path to input albedo AOV (optional)
- -n [string] : path to input normal AOV (optional, requires albedo AOV)
- -hdr [int] : Image is a HDR image. Disabling with will assume the image is in sRGB (default 1 i.e. enabled)
- -srgb [int] : whether the main input image is encoded with the sRGB (or 2.2 gamma) curve (LDR only) or is linear (default 0 i.e. disabled)
- -t [int] : number of threads to use (defualt is all)
- -affinity [int] : Enable affinity. This pins virtual threads to physical cores and can improve performance (default 0 i.e. disabled)
- -repeat [int] : Execute the denoiser N times. Useful for profiling.
- -maxmem [int] : Maximum memory size used by the denoiser in MB
- -clean_aux [int]: Whether the auxiliary feature (albedo, normal) images are noise-free; recommended for highest quality but should not be enabled for noisy auxiliary images to avoid residual noise (default 0 i.e. disabled)
- -h/--help : Lists command line parameters
You need to at least have an input and output for the app to run. If you also have them, you can add an albedo AOV or albedo and normal AOVs to improve the denoising. All images should be the same resolutions, not meeting this requirement will lead to unexpected results (likely a crash).
For best results provide as many of the AOVs as possible to the denoiser. Generally the more information the denoiser has to work with the better. The denoiser also prefers images rendered with a box filter or by using FIS.
Please refer to the originial OIDN repository here for more information.
This work is licensed under the MIT License, please see LICENSE file for more details