LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising

Yuxing Duan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 25637-25647

Event camera has significant advantages in capturingdynamic scene information while being prone to noise interferenceparticularly in challenging conditions like lowthreshold and low illumination. However most existing researchfocuses on gentle situations hindering event cameraapplications in realistic complex scenarios. To tackle thislimitation and advance the field we construct a new pairedreal-world event denoising dataset (LED) including 3K sequenceswith 18K seconds of high-resolution (1200*680)event streams and showing three notable distinctions comparedto others: diverse noise levels and scenes largerscalewith high-resolution and high-quality GT. Specificallyit contains stepped parameters and varying illuminationwith diverse scenarios. Moreover based on theproperty of noise events inconsistency and signal eventsconsistency we propose a novel effective denoising framework(DED) using homogeneous dual events to generate theGT with better separating noise from the raw. Furthermorewe design a bio-inspired baseline leveraging Leaky-Integrate-and-Fire (LIF) neurons with dynamic thresholdsto realize accurate denoising. The experimental resultsdemonstrate that the remarkable performance of the proposedapproach on different datasets.The dataset and codeare at https://github.com/Yee-Sing/led.