We present a solution for the goal of extracting a video from a single motion blurred image to sequentially reconstruct the clear views of a scene as beheld by the camera during the time of exposure. We first learn motion representation from sharp videos in an unsupervised manner through training of a convolutional recurrent video autoencoder network that performs a surrogate task of video reconstruction. Once trained, it is employed for guided training of a motion encoder for blurred images. This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder. As an intermediate step, we also design an efficient architecture that enables real-time single image deblurring and outperforms competing methods across all factors: accuracy, speed, and compactness. Experiments on real scenes and standard datasets demonstrate the superiority of our framework over the state-of-the-art and its ability to generate a plausible sequence of temporally consistent sharp frames.

翻译：我们提出了一个解决方案,目的是从单一运动的模糊图像中提取一个视频,以便按顺序重建摄像头在曝光时所看到的清晰场景的清晰视图。我们首先通过培训一个执行视频重建代理任务的革命性经常性视频自动编码网络,以不受监督的方式从锐利视频中学习运动演示。经过培训后,它被用于指导对模糊图像的动作编码器的培训。这个网络从模糊图像中提取嵌入的移动信息,以便与经过训练的经常性视频解码器一起生成一个锐利的视频。作为一个中间步骤,我们还设计了一个高效的架构,使实时单一图像能够破碎并超越所有因素的竞合方法:精确度、速度和紧凑性。对真实场景的实验和标准数据集表明我们框架优于最新工艺,以及它能够产生一个符合时间的直线框架的可靠序列。