量子电子学报 ›› 2026, Vol. 43 ›› Issue (5): 748-759.doi: 10.3969/j.issn.1007-5461.2026.05.007

• 图像与信息处理 • 上一篇    下一篇

基于高效通道注意力机制的事件相机图像快速重建

向昊晟 1,2, 梁清华 2, 王政 2, 丁瑞军 1,2*   

  1. 1 上海科技大学信息科学与技术学院, 上海 201210; 2 中国科学院上海技术物理研究所红外探测全国重点实验室, 上海 2000
  • 收稿日期:2025-01-16 修回日期:2025-03-10 出版日期:2026-09-28 发布日期:2026-09-30
  • 通讯作者: E-mail: dingrj@mail.sitp.ac.cn E-mail:dingrj@mail.sitp.ac.cn
  • 作者简介:向昊晟 ( 2000 - ), 湖北宜昌人, 研究生, 主要从事红外动态视觉相机处理系统和图像重建方面的研究。E-mail: xianghch2000@163.com
  • 基金资助:
    中国科学院基础与交叉前沿科研先导专项 (XDB0980000)

Fast image reconstruction for event cameras based on efficient channel attention mechanism

XIANG Haocheng 1,2, LIANG Qinghua 2, WANG Zheng 2, DING Ruijun 1,2*   

  1. 1 School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China; 2 National Key Laboratory of Infrared Detection Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
  • Received:2025-01-16 Revised:2025-03-10 Published:2026-09-28 Online:2026-09-30

摘要: 事件相机是一种类脑异步传感器, 能够以微秒级时间分辨率输出高动态范围、无运动模糊的事件数据, 直接 捕捉场景中的变化信息。通过深度学习将事件流重建为图像, 可应用于多种视觉任务。然而, 现有神经网络模型在 事件重建中常面临高复杂度、高计算成本, 或因过度轻量化而重建效果欠佳的问题。针对这些挑战, 本文提出一种结 合循环卷积网络与高效通道注意力机制的事件重建网络, 实现了高效快速的图像重建。实验结果表明, 与现有的重 构方法相比, 所提方法在针对不同类型的事件相机数据集的重建上显著提升了图像重建性能, 其均方误差、结构相似 性和感知相似性损失参数分别达到0.061、0.60 和0.38, 同时确保了模型轻量化与高效性。

关键词: 事件相机, 图像重构, 神经网络, 深度学习

Abstract: An event camera is a neuromorphic asynchronous sensor that can output event data with high
dynamic range and no motion blur at microsecond-level temporal resolution, directly capturing change
information in a scene. Deep learning-based image reconstruction from event streams shows promise for
various vision tasks but often suffers from high complexity, high computational cost, or poor
reconstruction quality in overly simplified models. To address these issues, this paper proposes an
efficient event reconstruction network combining a recurrent convolutional architecture with an efficient
channel attention mechanism. Experimental results show that, compared with existing reconstruction
methods, the proposed reconstruction network demonstrates improved performance on different types of
standard event camera datasets, achieving mean squared error, structural similarity index, and perceptual
similarity loss scores of 0.061, 0.60, and 0.38, respectively, while ensuring lightweight and efficient
operation.

Key words: event camera, image reconstruction, neural network, deep learning

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