To image,or not to image:class-specific diffractive cameras with all-optical erasure of undesired objects  

作　　者：Bijie Bai Yi Luo Tianyi Gan Jingtian Hu Yuhang Li Yifan Zhao Deniz Mengu Mona Jarrahi Aydogan Ozcan 

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

出　　处：《eLight》2022年第1期165-184,共20页e光学（英文）

基　　金：The Ozcan Research Group at UCLA acknowledges the support of ONR(Grant#N00014-22-1-2016);Jarrahi Research Group at UCLA acknowledges the support of the Department of Energy(Grant#DE-SC0016925).

摘　　要：Privacy protection is a growing concern in the digital era,with machine vision techniques widely used throughout public and private settings.Existing methods address this growing problem by,e.g.,encrypting camera images or obscuring/blurring the imaged information through digital algorithms.Here,we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects.This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input field-of-view.After their fabrication,the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes,while instantaneously erasing objects of the other data classes at the output field-of-view.Using the same framework,we also demonstrate the design of class-specific permutation and class-specific linear transformation cameras,where the objects of a target data class are pixel-wise permuted or linearly transformed following an arbitrarily selected transformation matrix for all-optical class-specific encryption,while the other classes of objects are irreversibly erased from the output image.The success of class-specific diffractive cameras was experimentally demonstrated using terahertz(THz)waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset,all-optically erasing the other handwritten digits.This diffractive camera design can be scaled to different parts of the electromagnetic spectrum,including,e.g.,the visible and infrared wavelengths,to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging.

关 键 词：optical IMAGE SPECIFIC 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]