DMCR-GAN: Adversarial Denoising for Monte Carlo Renderings with Residual Attention Networks and Hierarchical Features Modulation of Auxiliary Buffers

Abstract

Learning-based denoising single-frame Monte Carlo rendering methods have achieved better rendering quality in the photo-realistic rendering research. However, most of the works ignore the rich information of auxiliary buffers and treat all features equally. In this paper, we propose an adversarial approach for denoising Monte Carlo renderings (DMCR-GAN) with residual attention networks and hierarchical features modulation of auxiliary buffers. Specifically, we use a residual in residual (RIR) structure to make the network deeper and ease the flow of low-frequency information. Moreover, we propose a convolution dense block group (CDBG) to extract hierarchical features of auxiliary buffers and then to modulate the noisy features in RIR structure. Furthermore, we propose a channel attention (CA) and spatial attention (SA) mechanism to exploit the inter-channel and inter-spatial dependencies of features. Compared with the state-of-The-Art methods, our approach can restore more high-frequency information of images.