  In this paper, we present DEMC, a deep Dual-Encoder network to remove Monte
Carlo noise efficiently while preserving details. Denoising Monte Carlo
rendering is different from natural image denoising since inexpensive
by-products (feature buffers) can be extracted in the rendering stage. Most of
them are noise-free and can provide sufficient details for image
reconstruction. However, these feature buffers also contain redundant
information, which makes Monte Carlo denoising different from natural image
denoising. Hence, the main challenge of this topic is how to extract useful
information and reconstruct clean images. To address this problem, we propose a
novel network structure, Dual-Encoder network with a feature fusion
sub-network, to fuse feature buffers firstly, then encode the fused feature
buffers and a noisy image simultaneously, and finally reconstruct a clean image
by a decoder network. Compared with the state-of-the-art methods, our model is
more robust on a wide range of scenes and is able to generate satisfactory
results in a significantly faster way.
