Video deblurring is a highly under-constrained problem due to the spatially
and temporally varying blur. An intuitive approach for video deblurring
includes two steps: a) detecting the blurry region in the current frame; b)
utilizing the information from clear regions in adjacent frames for current
frame deblurring. To realize this process, our idea is to detect the pixel-wise
blur level of each frame and combine it with video deblurring. To this end, we
propose a novel framework that utilizes the motion magnitude prior (MMP) as
guidance for efficient deep video deblurring. Specifically, as the pixel
movement along its trajectory during the exposure time is positively correlated
to the level of motion blur, we first use the average magnitude of optical flow
from the high-frequency sharp frames to generate the synthetic blurry frames
and their corresponding pixel-wise motion magnitude maps. We then build a
dataset including the blurry frame and MMP pairs. The MMP is then learned by a
compact CNN by regression. The MMP consists of both spatial and temporal blur
level information, which can be further integrated into an efficient recurrent
neural network (RNN) for video deblurring. We conduct intensive experiments to
validate the effectiveness of the proposed methods on the public datasets.

提出了一种基于运动大小先验的深度学习视频去模糊技术，通过检测每一帧的像素级模糊程度，结合清晰帧区域的信息，生成像素运动大小先验，进而利用时空信息进行去模糊处理，实验结果表明该方法的有效性。