Event cameras are bio-inspired cameras which can measure the change of
intensity asynchronously with high temporal resolution. One of the event
cameras' advantages is that they do not suffer from motion blur when recording
high-speed scenes. In this paper, we formulate the deblurring task on
traditional cameras directed by events to be a residual learning one, and we
propose corresponding network architectures for effective learning of
deblurring and high frame rate video generation tasks. We first train a
modified U-Net network to restore a sharp image from a blurry image using
corresponding events. Then we train another similar network with different
downsampling blocks to generate high frame rate video using the restored sharp
image and events. Experiment results show that our method can restore sharper
images and videos than state-of-the-art methods.

本研究通过构建新的神经网络结构，使用事件相机实现了高清无暇的图像和视频的生成，并将故障模糊问题转化为残差学习的任务来有效地进行训练。实验结果表明，该方法可以比现有技术更好地还原图像和视频。