We propose a computational imaging method for time-efficient light-field acquisition that combines a coded aperture with an event-based camera. Different from the conventional coded-aperture imaging method, our method applies a sequence of coding patterns during a single exposure for an image frame. The parallax information, which is related to the differences in coding patterns, is recorded as events. The image frame and events, all of which are measured in a single exposure, are jointly used to computationally reconstruct a light field. We also designed an algorithm pipeline for our method that is end-to-end trainable on the basis of deep optics and compatible with real camera hardware. We experimentally showed that our method can achieve more accurate reconstruction than several other imaging methods with a single exposure. We also developed a hardware prototype with the potential to complete the measurement on the camera within 22 msec and demonstrated that light fields from real 3-D scenes can be obtained with convincing visual quality. Our software and supplementary video are available from our project website.

我们提出了一种利用编码孔径和事件相机结合的计算成像方法，通过在单次曝光中应用一系列编码模式来记录视差信息，并使用图像帧和事件共同计算重建光场的算法流程，该方法可在单次曝光中实现比其他成像方法更准确的重建，并且在相机上能够以22毫秒内完成测量的硬件原型表现出令人信服的视觉质量。

利用编码孔径和事件实现高效光场采集