  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.
