All-optical image classification through unknown random diffusers using a single-pixel diffractive network  

作　　者：Bijie Bai Yuhang Li Yi Luo Xurong Li Ege Cetintas Mona Jarrrahi Aydogan Ozcan 

机构地区：[1]Electrical and Computer Engineering Department,University of California,Los Angeles,California 90095,USA [2]Bioengineering Department,University of California,Los Angeles,California 90095,USA [3]California Nano Systems Institute(CNSI),University of California,Los Angeles,California 90095,USA

出　　处：《Light(Science & Applications)》2023年第4期570-584,共15页光（科学与应用)（英文版）

基　　金：support of the US Office of Naval Research(ONR).

摘　　要：Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields.Recent deep learning-based approaches demonstrated the classification of objects using diffuser-distorted patterns collected by an image sensor.These methods demand relatively large-scale computing using deep neural networks running on digital computers.Here,we present an all-optical processor to directly classify unknown objects through unknown,random phase diffusers using broadband illumination detected with a single pixel.A set of transmissive diffractive layers,optimized using deep learning,forms a physical network that all-optically maps the spatial information of an input object behind a random diffuser into the power spectrum of the output light detected through a single pixel at the output plane of the diffractive network.We numerically demonstrated the accuracy of this framework using broadband radiation to classify unknown handwritten digits through random new diffusers,never used during the training phase,and achieved a blind testing accuracy of 87.74±1.12%.We also experimentally validated our single-pixel broadband diffractive network by classifying handwritten digits"0"and"1"through a random diffuser using terahertz waves and a 3D-printed diffractive network.This single-pixel all-optical object classification system through random diffusers is based on passive diffractive layers that process broadband input light and can operate at any part of the electromagnetic spectrum by simply scaling the diffractive features proportional to the wavelength range of interest.These results have various potential applications in,e.g.,biomedical imaging,security,robotics,and autonomous driving.

关 键 词：network PROCESSOR RANDOM 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]