Video restoration tasks, including super-resolution, deblurring, etc, are
drawing increasing attention in the computer vision community. A challenging
benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark
challenges existing methods from two aspects: (1) how to align multiple frames
given large motions, and (2) how to effectively fuse different frames with
diverse motion and blur. In this work, we propose a novel Video Restoration
framework with Enhanced Deformable networks, termed EDVR, to address these
challenges. First, to handle large motions, we devise a Pyramid, Cascading and
Deformable (PCD) alignment module, in which frame alignment is done at the
feature level using deformable convolutions in a coarse-to-fine manner. Second,
we propose a Temporal and Spatial Attention (TSA) fusion module, in which
attention is applied both temporally and spatially, so as to emphasize
important features for subsequent restoration. Thanks to these modules, our
EDVR wins the champions and outperforms the second place by a large margin in
all four tracks in the NTIRE19 video restoration and enhancement challenges.
EDVR also demonstrates superior performance to state-of-the-art published
methods on video super-resolution and deblurring. The code is available at
this https URL

视频修复项目，涉及超分辨率、去模糊等多个任务。本文提出了一种名为 EDVR 的基于增强可变形网络的视频修复框架，通过金字塔级别的可变形卷积对齐帧以及时间空间注意力融合模块对图像进行处理，取得了 NTIRE19 视觉修复挑战的所有四个任务的冠军，并在视频超分辨率和去模糊方面表现优异。

EDVR: 基于增强型可变形卷积网络的视频修复

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

We present a deep-learning approach to restore a sequence of
turbulence-distorted video frames from turbulent deformations and space-time
varying blurs. Instead of requiring a massive training sample size in deep
networks, we purpose a training strategy that is based on a new data
augmentation method to model turbulence from a relatively small dataset. Then
we introduce a subsampled method to enhance the restoration performance of the
presented GAN model. The contributions of the paper is threefold: first, we
introduce a simple but effective data augmentation algorithm to model the
turbulence in real life for training in the deep network; Second, we firstly
purpose the Wasserstein GAN combined with $\ell_1$ cost for successful
restoration of turbulence-corrupted video sequence; Third, we combine the
subsampling algorithm to filter out strongly corrupted frames to generate a
video sequence with better quality.

本文提出一种基于深度学习方法的视频去抖动和去模糊的方法，使用数据增强算法对湍流进行建模，使用 Wasserstein GAN 和 $l1$ 损失函数进行训练和还原，并结合子采样算法提高视频质量。