Meta-Device for Field-of-View Tunability via Adaptive Optical Spatial Differentiation.

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.