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.

翻译：我们提出一种通过修改网络架构和损失函数将去噪神经网络从空间域转换为时空域的方法。我们在网络图的任意深度插入鲁棒平均模块。每个模块通过可训练权重执行潜在空间插值，并作用于网络前序空间组件输出的图像表征序列。通过强制网络从输入序列子集中预测去噪帧，时域连接在训练期间保持活跃。利用时域相干性进行去噪可提升图像质量并减少时域闪烁，且效果不受场景或图像复杂度影响。