the spir-v here is automatically generated from the oidn onnx.
this module requires a data/oidn-weights.dat file containing
the pre-trained weights for the oidn light ldr network.
if the input exposes some kind of inter-pixel correlation (such as from a
preceeding demosaicing operation) this module will not do anything at all. oidn
will classify the correlated noise statistics as signal and pass them on
unchagned. if you have a very noisy mosaic image, use half size as
demosaicing method to keep pixels uncorrelated. avoid resampling nodes
before denoising.
input scene-referred data, preferrably low-res and in [0,1]output the denoised imageit is my understanding that oidn has been trained on purely synthetic data, output from rendering systems. thus, no internet scraping or violation of rights of third parties took place during training.