  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 88.53%.
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.
