Real-time Monte Carlo denoising aims at removing severe noise under low samples per pixel (spp) in a strict time budget. Recently, kernel-prediction methods use a neural network to predict each pixel's filtering kernel and have shown a great potential to remove Monte Carlo noise. However, the heavy computation overhead blocks these methods from real-time applications. This paper expands the kernel-prediction method and proposes a novel approach to denoise very low spp (e.g., 1-spp) Monte Carlo path traced images at real-time frame rates. Instead of using the neural network to directly predict the kernel map, i.e., the complete weights of each per-pixel filtering kernel, we predict an encoding of the kernel map, followed by a high-efficiency decoder with unfolding operations for a high-quality reconstruction of the filtering kernels. The kernel map encoding yields a compact single-channel representation of the kernel map, which can significantly reduce the kernel-prediction network's throughput. In addition, we adopt a scalable kernel fusion module to improve denoising quality. The proposed approach preserves kernel prediction methods’ denoising quality while roughly halving its denoising time for 1-spp noisy inputs. In addition, compared with the recent neural bilateral grid-based real-time denoiser, our approach benefits from the high parallelism of kernel-based reconstruction and produces better denoising results at equal time.

中文翻译：

---

使用权重共享内核预测网络的实时蒙特卡罗去噪

实时蒙特卡罗去噪旨在在严格的时间预算内去除每像素低样本 (spp) 下的严重噪声。最近，核预测方法使用神经网络来预测每个像素的滤波核，并显示出消除蒙特卡洛噪声的巨大潜力。然而，沉重的计算开销阻碍了这些方法的实时应用。本文扩展了核预测方法，并提出了一种以实时帧速率对非常低的 spp（例如，1-spp）蒙特卡罗路径跟踪图像进行去噪的新方法。我们不是使用神经网络直接预测内核映射，即每个像素过滤内核的完整权重，而是预测内核映射的编码，然后是具有展开操作的高效解码器以获得高质量滤波内核的重建。内核映射编码产生内核映射的紧凑单通道表示，这可以显着降低内核预测网络的吞吐量。此外，我们采用可扩展的核融合模块来提高去噪质量。所提出的方法保留了核预测方法的去噪质量，同时将其对 1-spp 噪声输入的去噪时间大致减半。此外，与最近的基于神经双边网格的实时去噪器相比，我们的方法受益于基于核重建的高并行性，并在同等时间产生更好的去噪结果。所提出的方法保留了核预测方法的去噪质量，同时将其对 1-spp 噪声输入的去噪时间大致减半。此外，与最近的基于神经双边网格的实时去噪器相比，我们的方法受益于基于核重建的高并行性，并在同等时间产生更好的去噪结果。所提出的方法保留了核预测方法的去噪质量，同时将其对 1-spp 噪声输入的去噪时间大致减半。此外，与最近的基于神经双边网格的实时去噪器相比，我们的方法受益于基于核重建的高并行性，并在同等时间产生更好的去噪结果。