Robust Average Networks for Monte Carlo Denoising

TL;DR

This paper introduces Robust Average Networks that enhance Monte Carlo denoising by integrating spatio-temporal processing, improving image quality and reducing flickering through latent space interpolation and temporal coherence.

Contribution

It proposes a novel network architecture with Robust Average blocks for converting spatial denoising networks into spatio-temporal ones, leveraging temporal coherence.

Findings

Improved denoising quality with reduced flickering.

Effective integration of temporal information during training.

Enhanced performance across various scene complexities.

Abstract

We present a method for converting denoising neural networks from spatial into spatio-temporal ones by modifying the network architecture and loss function. We insert Robust Average blocks at arbitrary depths in the network graph. Each block performs latent space interpolation with trainable weights and works on the sequence of image representations from the preceding spatial components of the network. The temporal connections are kept live during training by forcing the network to predict a denoised frame from subsets of the input sequence. Using temporal coherence for denoising improves image quality and reduces temporal flickering independent of scene or image complexity.