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Transforming high-resolution imaging: A comprehensive review of advances in metasurfaces and metalenses
Materials Today Physics ( IF 10.0 ) Pub Date : 2024-12-14 , DOI: 10.1016/j.mtphys.2024.101628 Nikolay Lvovich Kazanskiy, Svetlana Nikolaevna Khonina, Muhammad Ali Butt
Materials Today Physics ( IF 10.0 ) Pub Date : 2024-12-14 , DOI: 10.1016/j.mtphys.2024.101628 Nikolay Lvovich Kazanskiy, Svetlana Nikolaevna Khonina, Muhammad Ali Butt
Meta-optics, particularly through the use of metasurfaces (MSs), have revolutionized high-resolution imaging (HRI) by enabling unprecedented control over light at the subwavelength scale. Metalenses (MLs), a key component of meta-optics, can correct chromatic aberrations and focus light with extreme precision, surpassing the limitations of traditional optics. Their compact design and ability to manipulate various wavelengths and polarizations of light allow for ultra-thin, lightweight imaging systems with enhanced resolution. These advancements are pushing the boundaries of imaging technologies in applications such as microscopy, sensing, and even consumer electronics. This review highlights recent advancements in MSs, with a focus on MLs for HRI. Additionally, the integration of deep learning techniques is explored, demonstrating enhanced imaging performance. Prospects and potential developments in ML technology are discussed, providing insights into their role in advancing next-generation imaging systems.
中文翻译:
改变高分辨率成像:超表面和超透镜进展的全面综述
超光学,特别是通过使用超表面 (MS),通过在亚波长尺度上实现对光的前所未有的控制,彻底改变了高分辨率成像 (HRI)。超透镜 (ML) 是超光学的关键部件,可以校正色差并以极高的精度聚焦光线,超越了传统光学的局限性。其紧凑的设计和操纵各种波长和光偏振的能力使超薄、轻便的成像系统具有更高的分辨率。这些进步正在推动成像技术在显微镜、传感甚至消费电子产品等应用中的界限。本综述重点介绍了 MS 的最新进展,重点关注 HRI 的 ML。此外,还探索了深度学习技术的集成,展示了增强的成像性能。讨论了 ML 技术的前景和潜在发展,并深入了解它们在推进下一代成像系统中的作用。
更新日期:2024-12-18
中文翻译:
改变高分辨率成像:超表面和超透镜进展的全面综述
超光学,特别是通过使用超表面 (MS),通过在亚波长尺度上实现对光的前所未有的控制,彻底改变了高分辨率成像 (HRI)。超透镜 (ML) 是超光学的关键部件,可以校正色差并以极高的精度聚焦光线,超越了传统光学的局限性。其紧凑的设计和操纵各种波长和光偏振的能力使超薄、轻便的成像系统具有更高的分辨率。这些进步正在推动成像技术在显微镜、传感甚至消费电子产品等应用中的界限。本综述重点介绍了 MS 的最新进展,重点关注 HRI 的 ML。此外,还探索了深度学习技术的集成,展示了增强的成像性能。讨论了 ML 技术的前景和潜在发展,并深入了解它们在推进下一代成像系统中的作用。