Monte Carlo Denoising via Auxiliary Feature Guided Self-Attention

作者：Yu, Jiaqi; Nie, Yongwei^*; Long, Chengjiang; Xu, Wenju; Zhang, Qing; Li, Guiqing

来源：ACM Transactions on Graphics, 2021, 40(6): 273.

DOI：10.1145/3478513.3480565

• Monte Carlo denoising
• self-attention
• non-local means

While self-attention has been successfully applied in a variety of natural language processing and computer vision tasks, its application in Monte Carlo (MC) image denoising has not yet been well explored. This paper presents a self-attention based MC denoising deep learning network based on the fact that self-attention is essentially non-local means filtering in the embedding space which makes it inherently very suitable for the denoising task. Particularly, we modify the standard self-attention mechanism to an auxiliary feature guided self-attention that considers the by-products (e.g., auxiliary feature buffers) of the MC rendering process. As a critical prerequisite to fully exploit the performance of self-attention, we design a multi-scale feature extraction stage, which provides a rich set of raw features for the later self-attention module. As self-attention poses a high computational complexity, we describe several ways that accelerate it. Ablation experiments validate the necessity and effectiveness of the above design choices. Comparison experiments show that the proposed self-attention based MC denoising method outperforms the current state-of-the-art methods.

• 单位
  中山大学