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<p>Electronic image stabilization technology for shipborne cameras enhances auxiliary navigation by providing critical status information and supporting decision-making in intelligent navigation systems. We have developed an optimized framework based on the image feature points to improve the stability and operational efficiency of these cameras. This method facilitates rapid processing while maintaining high image stability through the integration of feature recognition, motion estimation, and image smoothing. Our approach significantly outperforms traditional methods in efficiency, reliability, and flexibility. In addition, we introduced a weighted smoothing algorithm for handling motion trajectories and analyzed the impact of the ship's movement on the camera. Using a transformation compensation matrix, our stabilization experiments demonstrated a reduction in mean square error by 44–64% and an increase in peak signal-to-noise ratio by 9–17%.</p>

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An electronic image stabilization algorithm for shipborne cameras based on image feature points and ship movement

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月27日

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  • Jiexuan Zhuang,
  • Chiming Wang,
  • Zhonghao Chen,
  • Yanan Li,
  • Rongjiong Wu,
  • Shunzhi Zhu,
  • Liangqing Guan,
  • Li Liu,
  • Wang Man

摘要

Electronic image stabilization technology for shipborne cameras enhances auxiliary navigation by providing critical status information and supporting decision-making in intelligent navigation systems. We have developed an optimized framework based on the image feature points to improve the stability and operational efficiency of these cameras. This method facilitates rapid processing while maintaining high image stability through the integration of feature recognition, motion estimation, and image smoothing. Our approach significantly outperforms traditional methods in efficiency, reliability, and flexibility. In addition, we introduced a weighted smoothing algorithm for handling motion trajectories and analyzed the impact of the ship's movement on the camera. Using a transformation compensation matrix, our stabilization experiments demonstrated a reduction in mean square error by 44–64% and an increase in peak signal-to-noise ratio by 9–17%.