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.

中文翻译：

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带有层嵌入的神经去噪

我们提出了一种对蒙特卡罗路径跟踪图像去噪的新方法，该方法使用来自单个样本的数据而不是依赖像素聚合。样本被划分为多个层，这些层分别进行过滤，使网络能够更自由地处理异常值和复杂的可见性。最后，这些层使用 alpha 混合从前到后合成。该系统经过端到端的训练，具有学习的层划分、过滤器内核和合成。我们以一小部分计算成本和内存要求获得与最近最先进的基于样本的降噪器相似的图像质量。