  The classic Monte Carlo path tracing can achieve high quality rendering at
the cost of heavy computation. Recent works make use of deep neural networks to
accelerate this process, by improving either low-resolution or fewer-sample
rendering with super-resolution or denoising neural networks in
post-processing. However, denoising and super-resolution have only been
considered separately in previous work. We show in this work that Monte Carlo
path tracing can be further accelerated by joint super-resolution and denoising
(SRD) in post-processing. This new type of joint filtering allows only a
low-resolution and fewer-sample (thus noisy) image to be rendered by path
tracing, which is then fed into a deep neural network to produce a
high-resolution and clean image. The main contribution of this work is a new
end-to-end network architecture, specifically designed for the SRD task. It
contains two cascaded stages with shared components. We discover that denoising
and super-resolution require very different receptive fields, a key insight
that leads to the introduction of deformable convolution into the network
design. Extensive experiments show that the proposed method outperforms
previous methods and their variants adopted for the SRD task.
