EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution

Jin Han, Yixin Yang, Chu Zhou, Chao Xu, Boxin Shi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4882-4891

An event camera detects the scene radiance changes and sends a sequence of asynchronous event streams with high dynamic range, high temporal resolution, and low latency. However, the spatial resolution of event cameras is limited as a trade-off for these outstanding properties. To reconstruct high-resolution intensity images from event data, we propose EvIntSR-Net that converts event data to multiple latent intensity frames to achieve super-resolution on intensity images in this paper. EvIntSR-Net bridges the domain gap between event streams and intensity frames and learns to merge a sequence of latent intensity frames in a recurrent updating manner. Experimental results show that EvIntSR-Net can reconstruct SR intensity images with higher dynamic range and fewer blurry artifacts by fusing events with intensity frames for both simulated and real-world data. Furthermore, the proposed EvIntSR-Net is able to generate high-frame-rate videos with super-resolved frames.