论文标题
学会使用事件摄像机来消除并产生高帧速率视频
Learning to Deblur and Generate High Frame Rate Video with an Event Camera
论文作者
论文摘要
事件摄像机是由生物启发的摄像机，可以通过高时间分辨率来测量强度的变化。事件相机的优势之一是，在录制高速场景时，它们不会遭受运动模糊的困扰。在本文中，我们在事件导演的传统摄像机上制定了脱俗的任务，这是一个残留的学习，我们提出了相应的网络体系结构，以有效学习Deblurring和高帧速率的视频生成任务。我们首先使用相应的事件训练修改后的U-NET网络，从模糊图像中恢复锋利的图像。然后，我们训练另一个具有不同下采样块的类似网络，以使用恢复的夏普图像和事件来生成高帧速率视频。实验结果表明，与最先进的方法相比，我们的方法可以恢复更清晰的图像和视频。
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.
