Zelin Zhang
Papers
2
Total Citations
33
H-Index
2
About
Zelin Zhang is a researcher specializing in event-based vision and computational imaging, with a particular focus on bridging bio-inspired sensor technology with advanced machine learning frameworks. His most recognized contribution centers on reformulating event-based image reconstruction as a linear inverse problem, a novel approach that incorporates deep regularization and optical flow to recover high-quality brightness images from event camera data. This work addresses a fundamental challenge in the field: unlocking the full potential of event cameras, which are neuromorphic sensors capable of capturing scenes with exceptionally high dynamic range and temporal resolution far beyond conventional frame-based cameras. By leveraging optical flow as a regularization prior within a principled mathematical framework, Zhang's method produces reconstructed images that preserve the inherent HDR and high-speed advantages of event data, making them directly applicable to robotic vision pipelines. His research has accumulated over 30 citations across related publications, reflecting meaningful uptake within the robotics and computer vision communities. Zhang's work represents an important step toward making event cameras practically viable for real-world perception tasks, positioning him as a promising contributor to the emerging field of neuromorphic visual computing.
Research Focus
Key Achievements
Top Papers
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