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Area of Science:

  • Computational optics
  • Artificial intelligence in optics
  • Deep learning for optical imaging

Background:

  • Traditional optical systems face limitations in sensing dimension, light throughput, and resolution.
  • Computational optics integrates computation to overcome these limitations, enhancing imaging and sensing.
  • Deep learning has significantly advanced computational optics, improving precision and efficiency.

Purpose of the Study:

  • To explore optical modulation elements for digital twin models in joint encoding-decoding optimization.
  • To address the challenge of reverse physical twinning from optimized parameters to practical modulation elements.
  • To provide guidance for selecting appropriate modulation elements in computational optics.

Main Methods:

  • Review of various optical modulation elements across spatial, phase, and spectral dimensions.
  • Analysis of digital twin models for joint encoding-decoding optimization.
  • Examination of the discrepancies between optimized encoding parameters and physical modulation elements.

Main Results:

  • Identification of challenges in reverse physical twinning due to gaps in bit depth, numerical range, and stability.
  • Exploration of diverse optical modulation elements within the digital twin framework.
  • Analysis of trade-offs between precision, speed, and robustness for different modulation elements.

Conclusions:

  • The digital twin model offers enhanced performance for computational optics.
  • Guidance is provided for selecting modulation elements based on specific imaging and sensing task requirements.
  • This review aims to facilitate the development of next-generation computational optics by addressing twinning challenges.