将超表面与算法结合，实现超越传统成像极限的智能光学系统。
Computational metaoptics for imaging
- 把超表面当作物理预处理器，端到端联合设计光学硬件与计算算法。
- 可同时实现多功能成像且保持高效率，突破单层超表面性能瓶颈。
- 适合做光学成像、量子态测量等复杂光场重构任务的研究者参考。
超表面——由亚波长光学元件构成的超薄结构——通过精确调控电磁波的振幅、相位、偏振和光谱特性，彻底革新了光操控方式。与此同时，计算成像利用算法从光学处理信号中重建图像，突破了传统成像系统的局限。本文综述了超表面与计算成像的协同融合——‘计算超表面’，该技术将超表面的物理波前调制能力与先进计算算法结合，显著提升成像性能，突破传统限制。文章探讨了计算超表面如何解决单层超表面在实现多功能性时难以兼顾效率的固有缺陷。通过将超表面视为物理预处理器，并采用端到端（逆向）设计联合优化光学硬件与重建算法，可自动发现最优超表面结构与重建方法，大幅增强成像能力。文中还展示了其在相位成像和量子态测量等前沿应用中的潜力，得益于超表面对复杂光场的操控能力及算法对高维信息的重建能力。此外，文章指出性能评估面临挑战，需建立涵盖光学与计算双重特性的新指标。最后，展望了计算超表面的新前沿，预示其将在未来成像科学与技术发展中扮演核心角色。
原文摘要 · Abstract (English)
Metasurfaces -- ultrathin structures composed of subwavelength optical elements -- have revolutionized light manipulation by enabling precise control over electromagnetic waves' amplitude, phase, polarization, and spectral properties. Concurrently, computational imaging leverages algorithms to reconstruct images from optically processed signals, overcoming limitations of traditional imaging systems. This review explores the synergistic integration of metaoptics and computational imaging, "computational metaoptics," which combines the physical wavefront shaping ability of metasurfaces with advanced computational algorithms to enhance imaging performance beyond conventional limits. We discuss how computational metaoptics addresses the inherent limitations of single-layer metasurfaces in achieving multifunctionality without compromising efficiency. By treating metasurfaces as physical preconditioners and co-designing them with reconstruction algorithms through end-to-end (inverse) design, it is possible to jointly optimize the optical hardware and computational software. This holistic approach allows for the automatic discovery of optimal metasurface designs and reconstruction methods that significantly improve imaging capabilities. Advanced applications enabled by computational metaoptics are highlighted, including phase imaging and quantum state measurement, which benefit from the metasurfaces' ability to manipulate complex light fields and the computational algorithms' capacity to reconstruct high-dimensional information. We also examine performance evaluation challenges, emphasizing the need for new metrics that account for the combined optical and computational nature of these systems. Finally, we identify new frontiers in computational metaoptics which point toward a future where computational metaoptics may play a central role in advancing imaging science and technology.
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