Bian Liheng, Zhan Xinrui, Yan Rong, Chang Xuyang, Huang Hua, Zhang Jun
State Key Laboratory of CNS/ATM & MIIT Key Laboratory of Complex-field Intelligent Sensing, Beijing Institute of Technology, Beijing & Zhuhai, China.
Yangtze Delta Region Academy of Beijing Institute of Technology (Jiaxing), Jiaxing, China.
Light Sci Appl. 2025 Apr 15;14(1):162. doi: 10.1038/s41377-025-01810-4.
Computational optics introduces computation into optics and consequently helps overcome traditional optical limitations such as low sensing dimension, low light throughput, low resolution, and so on. The combination of optical encoding and computational decoding offers enhanced imaging and sensing capabilities with diverse applications in biomedicine, astronomy, agriculture, etc. With the great advance of artificial intelligence in the last decade, deep learning has further boosted computational optics with higher precision and efficiency. Recently, there developed an end-to-end joint optimization technique that digitally twins optical encoding to neural network layers, and then facilitates simultaneous optimization with the decoding process. This framework offers effective performance enhancement over conventional techniques. However, the reverse physical twinning from optimized encoding parameters to practical modulation elements faces a serious challenge, due to the discrepant gap in such as bit depth, numerical range, and stability. In this regard, this review explores various optical modulation elements across spatial, phase, and spectral dimensions in the digital twin model for joint encoding-decoding optimization. Our analysis offers constructive guidance for finding the most appropriate modulation element in diverse imaging and sensing tasks concerning various requirements of precision, speed, and robustness. The review may help tackle the above twinning challenge and pave the way for next-generation computational optics.
计算光学将计算引入光学领域，从而有助于克服传统光学的局限性，如传感维度低、光通量低、分辨率低等。光学编码与计算解码的结合提供了增强的成像和传感能力，在生物医学、天文学、农业等领域有多种应用。随着过去十年人工智能的巨大进步，深度学习以更高的精度和效率进一步推动了计算光学的发展。最近，开发了一种端到端联合优化技术，该技术将光学编码数字孪生到神经网络层，然后在解码过程中促进同步优化。该框架比传统技术提供了有效的性能提升。然而，从优化的编码参数到实际调制元件的反向物理孪生面临着严峻挑战，这是由于在比特深度、数值范围和稳定性等方面存在差异。在这方面，本综述探讨了数字孪生模型中跨空间、相位和光谱维度的各种光学调制元件，以进行联合编码 - 解码优化。我们的分析为在涉及精度、速度和鲁棒性等各种要求的不同成像和传感任务中找到最合适的调制元件提供了建设性指导。该综述可能有助于应对上述孪生挑战，并为下一代计算光学铺平道路。
