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<p>In surveillance camera systems and other human-image analysis systems, it is important to measure human shapes with high resolution. However, the spatial resolution and image quality achievable through approaches based solely on optical design and image processing are fundamentally limited by hardware constraints and the inherent difficulty of the inverse problems involved. To overcome these limitations, we propose a super resolution imaging system for human silhouettes based on a jointly-optimized design involving coded illumination patterns and reconstruction networks. Our proposed method allows for the acquisition of human silhouette data with improved sampling resolution without modifying the camera hardware. We quantitatively demonstrated the effectiveness of our proposed method through simulations and also through optical experiments using a projector and a camera.</p>

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Super-resolution human-silhouette imaging by joint optimization of coded illumination and reconstruction network: a simulation study

  • 期刊论文

  • 发表日期：

    2025年1月24日

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  • Shunsuke Sakoda,
  • Tomoya Nakamura,
  • Yasushi Yagi

摘要

In surveillance camera systems and other human-image analysis systems, it is important to measure human shapes with high resolution. However, the spatial resolution and image quality achievable through approaches based solely on optical design and image processing are fundamentally limited by hardware constraints and the inherent difficulty of the inverse problems involved. To overcome these limitations, we propose a super resolution imaging system for human silhouettes based on a jointly-optimized design involving coded illumination patterns and reconstruction networks. Our proposed method allows for the acquisition of human silhouette data with improved sampling resolution without modifying the camera hardware. We quantitatively demonstrated the effectiveness of our proposed method through simulations and also through optical experiments using a projector and a camera.