论文标题
第一个图像然后视频：用于时空视频的两阶段网络
First image then video: A two-stage network for spatiotemporal video denoising
论文作者
论文摘要
视频降解是为了从噪声浪费的数据中消除噪声，从而通过时空处理恢复了真实的信号。现有的时空视频降级方法往往会遭受运动模糊伪影的损失，即，移动物体的边界往往会看起来模糊，尤其是当对象发生快速运动，导致光流计算分解时。在本文中，我们通过设计第一图像 - 视频两阶段的神经网络来应对这一挑战，该网络由图像Denoising模块组成，用于空间降低框架内噪声，然后是常规的时空视频DeNoising模块。直觉是简单而有效的：图像DeNoising的第一阶段有效地降低了噪声水平，因此允许时空转化的第二阶段，以更好地建模和学习到各地，包括沿着移动的对象界限。这个两阶段的网络以端到端的方式接受培训时，就可以在降级质量和计算方面产生视频降级基准VIMEO90K数据集的最新性能。它还使无监督的方法与现有监督方法相当。
Video denoising is to remove noise from noise-corrupted data, thus recovering true signals via spatiotemporal processing. Existing approaches for spatiotemporal video denoising tend to suffer from motion blur artifacts, that is, the boundary of a moving object tends to appear blurry especially when the object undergoes a fast motion, causing optical flow calculation to break down. In this paper, we address this challenge by designing a first-image-then-video two-stage denoising neural network, consisting of an image denoising module for spatially reducing intra-frame noise followed by a regular spatiotemporal video denoising module. The intuition is simple yet powerful and effective: the first stage of image denoising effectively reduces the noise level and, therefore, allows the second stage of spatiotemporal denoising for better modeling and learning everywhere, including along the moving object boundaries. This two-stage network, when trained in an end-to-end fashion, yields the state-of-the-art performances on the video denoising benchmark Vimeo90K dataset in terms of both denoising quality and computation. It also enables an unsupervised approach that achieves comparable performance to existing supervised approaches.
