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+Intel Open Image Denoise
+
+This build does NOT build support for CUDA/Xe/RDNA, patches welcome.
+
+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.
+