Various blur distortions in video will cause negative impact on both human
viewing and video-based applications, which makes motion-robust deblurring
methods urgently needed. Most existing works have strong dataset dependency and
limited generalization ability in handling challenging scenarios, like blur in
low contrast or severe motion areas, and non-uniform blur. Therefore, we
propose a PRiOr-enlightened and MOTION-robust video deblurring model
(PROMOTION) suitable for challenging blurs. On the one hand, we use 3D group
convolution to efficiently encode heterogeneous prior information, explicitly
enhancing the scenes' perception while mitigating the output's artifacts. On
the other hand, we design the priors representing blur distribution, to better
handle non-uniform blur in spatio-temporal domain. Besides the classical camera
shake caused global blurry, we also prove the generalization for the downstream
task suffering from local blur. Extensive experiments demonstrate we can
achieve the state-of-the-art performance on well-known REDS and GoPro datasets,
and bring machine task gain.

该研究提出了一种 PRiOr 启发的、适用于具有挑战性的模糊的 MOTION-robust 视频去模糊模型 (PROMOTION), 其中使用 3D 群卷积来有效编码异质先验信息，增强场景感知，同时减少输出的伪影，并设计表示模糊分布的先验，以更好地在时空域中处理非均匀模糊。经过大量实验证明，该方法可以在 RED 和 GoPro 数据集上实现最先进的性能，并带来机器任务收益。

先验知识与动态稳健视频去模糊

Prior-enlightened and Motion-robust Video Deblurring

Non-uniform blur, mainly caused by camera shake and motions of multiple
objects, is one of the most common causes of image quality degradation.
However, the traditional blind deblurring methods based on blur kernel
estimation do not perform well on complicated non-uniform motion blurs. Recent
studies show that GAN-based approaches achieve impressive performance on
deblurring tasks. In this letter, to further improve the performance of
GAN-based methods on deblurring tasks, we propose an edge heuristic multi-scale
generative adversarial network(GAN), which uses the "coarse-to-fine" scheme to
restore clear images in an end-to-end manner. In particular, an edge-enhanced
network is designed to generate sharp edges as auxiliary information to guide
the deblurring process. Furthermore, We propose a hierarchical content loss
function for deblurring tasks. Extensive experiments on different datasets show
that our method achieves state-of-the-art performance in dynamic scene
deblurring.

提出了一种基于边缘启发式多尺度生成对抗网络的图像去模糊方法，采用粗到细的方法进行端到端去模糊，并设计了边缘增强网络和分层内容损失函数来指导去模糊过程，在不同数据集上进行了广泛实验，取得了动态场景去模糊的最佳性能。

非均匀盲模糊的边缘启发式生成对抗网络

Edge Heuristic GAN for Non-uniform Blind Deblurring

The image blurring process is generally modelled as the convolution of a blur
kernel with a latent image. Therefore, the estimation of the blur kernel is
essentially important for blind image deblurring. Unlike existing approaches
which focus on approaching the problem by enforcing various priors on the blur
kernel and the latent image, we are aiming at obtaining a high quality blur
kernel directly by studying the problem in the frequency domain. We show that
the auto-correlation of the absolute phase-only image can provide faithful
information about the motion (e.g. the motion direction and magnitude, we call
it the motion pattern in this paper.) that caused the blur, leading to a new
and efficient blur kernel estimation approach. The blur kernel is then refined
and the sharp image is estimated by solving an optimization problem by
enforcing a regularization on the blur kernel and the latent image. We further
extend our approach to handle non-uniform blur, which involves spatially
varying blur kernels. Our approach is evaluated extensively on synthetic and
real data and shows good results compared to the state-of-the-art deblurring
approaches.

本文提出了一种基于自相关的相位图像的频域方法，直接从运动模式中提取高质量的模糊核，在加入对模糊核和潜在图像的正则化约束下，优化求解模糊核和清晰图像的问题，从而实现图像去模糊。此方法可处理空间变化和非均匀模糊，并在合成和真实数据上得到了良好的结果。