EIC-LIE uses an event-illumination collaborative module and illumination-aware event filter plus a new real-world dataset to improve low-light image enhancement over prior methods.
A unifying contrast maximization framework for event cam- eras, with applications to motion, depth, and optical flow estimation
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Introduces the first large-scale event-based HAR benchmark under low-light and 6-DoF shaking conditions with IMU data, and an EIS-HAR pipeline using non-linear warping for motion compensation plus a four-stage hybrid network that outperforms prior methods on three datasets.
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Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset
EIC-LIE uses an event-illumination collaborative module and illumination-aware event filter plus a new real-world dataset to improve low-light image enhancement over prior methods.
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DarkShake-DVS: Event-based Human Action Recognition under Low-light andShaking Camera Conditions
Introduces the first large-scale event-based HAR benchmark under low-light and 6-DoF shaking conditions with IMU data, and an EIS-HAR pipeline using non-linear warping for motion compensation plus a four-stage hybrid network that outperforms prior methods on three datasets.