基于超表面的单像素识别透过散射介质

Taking advantage of optoelectronic hybrid neural networks, we propose a metasurface-single-pixel hybrid neural network for object recognition. It employs only eight illumination patterns trained by the digital neural network to convolve the object from two-dimensional images into only eight intensity values measured by a single-pixel detector, achieving a 93.8% accuracy rate in handwritten digit recognition. Our work therefore paves an image-free way for metasurface-based object recognition using only a single-pixel detector, which exhibits its powerful information compression and accurate extraction capabilities coupled with a compact structural design.