<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yang, Yixin</style></author><author><style face="normal" font="default" size="100%">Liang, Jinxiu</style></author><author><style face="normal" font="default" size="100%">Yu, Bohan</style></author><author><style face="normal" font="default" size="100%">Yan Chen</style></author><author><style face="normal" font="default" size="100%">Ren, Jimmy S.</style></author><author><style face="normal" font="default" size="100%">Shi, Boxin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Latency Correction for Event-guided Deblurring and Frame Interpolation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><pages><style face="normal" font="default" size="100%">24977–24986</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 – 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.</style></abstract></record></records></xml>