Computational imaging without a computer:seeing through random diffusers at the speed of light  

作　　者：Yi Luo Yifan Zhao Jingxi Li Ege Çetintaş Yair Rivenson Mona Jarrahi Aydogan Ozcan 

机构地区：[1]Electrical and Computer Engineering Department,University of California,Los Angeles,Los Angeles,CA 90095,USA [2]Bioengineering Department,University of California,Los Angeles,Los Angeles,CA 90095,USA [3]California NanoSystems Institute,University of California,Los Angeles,Los Angeles,CA 90095,USA.

出　　处：《eLight》2022年第1期42-57,共16页e光学（英文）

基　　金：The authors acknowledge the U.S.National Science Foundation and Fujikura.

摘　　要：Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers.Here,we present a computer-free,all-optical image reconstruction method to see through random diffusers at the speed of light.Using deep learning,a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown,random phase diffusers.After the training stage,which is a one-time effort,the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown,new phase diffuser.We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown,random diffusers,never used during training.Unlike digital methods,all-optical diffractive reconstructions do not require power except for the illumination light.This diffractive solution to see through diffusers can be extended to other wavelengths,and might fuel various applications in biomedical imaging,astronomy,atmospheric sciences,oceanography,security,robotics,autonomous vehicles,among many others.

关 键 词：Imaging through diffusers Computational imaging Diffractive neural network Deep learning 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]