@conference {yang2024latency,
	title = {Latency Correction for Event-guided Deblurring and Frame Interpolation},
	booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2024},
	pages = {24977{\textendash}24986},
	abstract = {Event cameras with their high temporal resolution dynamic range and low power consumption are particularly good at time-sensitive applications like deblurring and frame interpolation. However their performance is hindered by latency variability especially under low-light conditions and with fast-moving objects. This paper addresses the challenge of latency in event cameras {\textendash} the temporal discrepancy between the actual occurrence of changes in the corresponding timestamp assigned by the sensor. Focusing on event-guided deblurring and frame interpolation tasks we propose a latency correction method based on a parameterized latency model. To enable data-driven learning we develop an event-based temporal fidelity to describe the sharpness of latent images reconstructed from events and the corresponding blurry images and reformulate the event-based double integral model differentiable to latency. The proposed method is validated using synthetic and real-world datasets demonstrating the benefits of latency correction for deblurring and interpolation across different lighting conditions.},
	author = {Yang, Yixin and Liang, Jinxiu and Yu, Bohan and Yan Chen and Ren, Jimmy S. and Shi, Boxin}
}
