Neural Denoising with Layer Embeddings

We propose a novel approach for denoising Monte Carlo path traced images, which uses data from individual samples rather than relying on pixel aggregates. Samples are partitioned into layers, which are filtered separately, giving the network more freedom to handle outliers and complex visibility. Finally the layers are composited front-to-back using alpha blending. The system is trained end-to-end, with learned layer partitioning, filter kernels, and compositing. We obtain similar image quality as recent state-of-the-art sample based denoisers at a fraction of the computational cost and memory requirements.

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