Event-based High Dynamic Range Image and Very High Frame Rate Video Generation using Conditional Generative Adversarial Networks

Abstract

Event cameras have a lot of advantages over traditional cameras, such as low latency, high temporal resolution, and high dynamic range. However, since the outputs of event cameras are the sequences of asynchronous events overtime rather than actual intensity images, existing algorithms could not be directly applied. Therefore, it is demanding to generate intensity images from events for other tasks. In this paper, we unlock the potential of event camera-based conditional generative adversarial networks to create images/videos from an adjustable portion of the event data stream. The stacks of space-time coordinates of events are used as inputs and the network is trained to reproduce images based on the spatio-temporal intensity changes. The usefulness of event cameras to generate high dynamic range(HDR) images even in extreme illumination conditions and also non blurred images under rapid motion is also shown.In addition, the possibility of generating very high frame rate videos is demonstrated, theoretically up to 1 million frames per second (FPS) since the temporal resolution of event cameras are about 1μs. Proposed methods are evaluated by comparing the results with the intensity images captured on the same pixel grid-line of events using online available real datasets and synthetic datasets produced by the event camera simulator.

arXiv comment10 pages,

References in corpus (2)

• The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM
• Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars

Cited by in corpus (11)

• Event-based Vision: A Survey
• Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks
• Event-Based Fusion for Motion Deblurring with Cross-modal Attention
• Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
• E-MLB: Multilevel Benchmark for Event-Based Camera Denoising
• HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks
• Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations
• EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction
• Automotive Object Detection via Learning Sparse Events by Spiking Neurons
• Data-Driven Feature Tracking for Event Cameras With and Without Frames
• E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations