在计算光学中进行联合编码解码优化的物理生:一篇综述
Liheng Bian1,2, Xinrui Zhan3, Rong Yan3
1State Key Laboratory of CNS/ATM & MIIT Key Laboratory of Complex-field Intelligent Sensing, Beijing Institute of Technology, Beijing & Zhuhai, China. bian@bit.edu.cn.
Light, science & applications
|April 14, 2025
概括
计算光学将光学编码与人工智能驱动的解码相结合,用于增强成像. 本综述指导选择调制元件用于计算光学中的实际数字双胞胎应用.
科学领域:
- 计算光学是一种计算光学.
- 在光学领域的人工智能
- 深度学习用于光学成像.
背景情况:
- 传统的光学系统在传感尺寸,光通量和分辨率方面面临限制.
- 计算光学集成计算来克服这些局限性,增强成像和传感.
- 深度学习已经显著提升了计算光学,提高了精度和效率.
研究的目的:
- 在联合编码解码优化中探索数字双胞胎模型的光学调制元件.
- 为了应对从优化参数到实际调制元件的反向物理结合的挑战.
- 为在计算光学中选择适当的调制元件提供指导.
主要方法:
- 通过空间,相位和光谱维度对各种光学调制元件的审查.
- 对数字双胞胎模型进行分析,以实现联合编码-解码优化.
- 检查优化编码参数和物理调制元件之间的差异.
主要成果:
- 鉴定由于比特深度,数值范围和稳定性的差距而导致的反向物理生中的挑战.
- 在数字双胞胎框架内探索各种光学调制元件.
- 对不同调制元件的精度,速度和稳定性之间的权衡分析.
结论:
- 数字双胞胎模型为计算光学提供了增强的性能.
- 根据特定的成像和传感任务要求,为选择调制元件提供了指导.
- 本次审查旨在通过解决结合挑战来促进下一代计算光学的发展.
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