Hengyi Lv

Papers

Total Citations

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About

Hengyi Lv is a leading researcher in neuromorphic vision and sensor signal processing, with a focus on advancing dynamic vision sensor (DVS) technology for real-world applications in autonomous vehicles and robotics. His most cited work, “Event Density Based Denoising Method for Dynamic Vision Sensor” (2020, 89 citations), introduces a novel approach to filtering background activity (BA) noise—a persistent challenge in DVS data. By leveraging event density patterns rather than traditional temporal or spatial thresholds, Lv’s method significantly improves signal fidelity, enabling more reliable perception in high-speed or low-light environments. This contribution addresses a critical bottleneck in neuromorphic sensing, where noisy data can compromise downstream tasks like object tracking or collision avoidance. Beyond this, Lv’s research spans efficient event-driven architectures and real-time processing pipelines, bridging the gap between sensor hardware and practical deployment. His work has been recognized for its impact on noise reduction benchmarks and has influenced subsequent designs in event-based vision systems. With a growing citation footprint, Hengyi Lv continues to shape the future of neuromorphic engineering, making DVS technology more robust and accessible for next-generation intelligent systems.