INTENSITY-IMAGE RECONSTRUCTION FOR EVENT CAMERAS USING CONVOLUTIONAL NEURAL NETWORK

Event cameras have many benefits than conventional cameras, such as high temporal resolution, high dynamic range. However, because the outputs of event cameras are asynchronous event streams than intensity images, Frame-based algorithms cannot be directly used. It is also necessary to present intensity images of event cameras on the display for human viewing. In this paper, "event frames" are recovered from event streams in an attenuation method and they are fed into the U-net network to generate intensity images. Our model is trained on a large amount of simulated data and gradually reduces the perceptual loss through training. In order to evaluate the model, we compare the generated image with the target image on the simulated data and the real data. This proves that our model can reconstruct intensity images of event cameras very well.