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Fast Denoising with Self Stabilizing Recurrent Blurs
Dmitry Zhdan, NVIDIA
In this topic NVIDIA is going to discuss latest advancements in non-DL based denoising. Basing on previous work from Metro Exodus, a new method has been introduced which is based on recurrent blur too, but it has got a lot of improvements, like: better overall performance, cleaner results, specular denoising support, fast data reconstruction, better bilateral weighting and some others. Besides the algorithm overview the topics will be covered in the talk: - mipmapping of incoming radiance - is it worth it? - proper controlling of blur radius to avoid over-blurring - accurate dis-occlusion detection - specular tracking without specular motion - fast and high amplitude noise free data reconstruction of regions with discarded history - exponential versus linear accumulation. Why linear accumulation is better? - how to compute bilateral weights in spatial passes? - input signal compression - should it be used or not? - how to fight with temporal lag