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结构转换为时空结构的方法。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的权重执行潜在空间插值，并对网络先前空间组件产生的图像表征序列进行处理。通过强制网络从输入序列的子集预测去噪帧，保持训练过程中时间连接的活性。利用时间相干性进行去噪可提升图像质量，并减少与场景或图像复杂度无关的时间闪烁现象。