Meta‐Device for Field‐of‐View Tunability via Adaptive Optical Spatial Differentiation
Abstract Optical edge detection is a crucial optical analog computing method in fundamental artificial intelligence, machine vision, and image recognition, owing to its advantages of parallel processing, high computing speed, and low energy consumption. Field‐of‐view‐tunable edge detection is partic...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202412794 |
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| Summary: | Abstract Optical edge detection is a crucial optical analog computing method in fundamental artificial intelligence, machine vision, and image recognition, owing to its advantages of parallel processing, high computing speed, and low energy consumption. Field‐of‐view‐tunable edge detection is particularly significant for detecting a broader range of objects, enhancing both practicality and flexibility. In this work, a novel approach—adaptive optical spatial differentiation is proposed for field‐of‐view‐tunable edge detection. This method improves the ability to acquire spatial information and facilitates edge detection over a wider angular range. The adaptive optical spatial differentiation meta‐device relies on two core components: the spatial differentiation dielectric metasurface and the adaptive liquid prism. The meta‐device is shown to function as a highly efficient (≈85%) isotropic spatial differentiator, operating across the entire visible spectrum (400 to 700 nm) within a wide‐angle object space, expanding up to 4.5 times the original field of view. The proposed scheme presents new opportunities for efficient, flexible, high‐capacity integrated data processing and imaging devices. And simultaneously provides a novel optical analog computing architecture for the next generation of wide field‐of‐view phase contrast microscopy. |
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| ISSN: | 2198-3844 |