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

Abstract

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.

Polarization-based all-optical logic gates using diffractive neural networks

“…During the training process, the design of the passive diffractive layers, or neurons, is optimized such that the network performs a specific function. D 2 NN has been applied to image recognition [29][30][31][32][33][35][36][37][38][39][40][41][42][43][44][45][46], optical logic operations [21,47,48], terahertz pulse shaping [49], phase retrieval [50], and image reconstruction [34,[51][52][53] etc.…”

Advanced all-optical classification using orbital-angular-momentum-encoded diffractive networks

“…Each pixel point on the diffractive layer is a parameter that can be learned by the computer and can be used for independent complex-valued tuning of the light field. Based on its capabilities in optical information processing, normalD2NN has been applied to image recognition, 11 , 26 – 40 optical logic operations, 41 – 43 terahertz pulse shaping, 44 phase retrieval, 45 and image reconstruction, 15 , 46 – 48 etc.…”

“…Diffractive networks performed in passive optical elements have the advantages of fast processing speed and low energy consumption, while also enabling flexible utilization of various degrees of freedom of light in the network. For example, when using broadband light instead of monochromatic light to illuminate the diffractive networks, spectrally encoded machine vision applications, 15 , 38 parallel computing, 39 snapshot multispectral imaging, 48 and spatially controlled wavelength multiplexing/demultiplexing 49 can be accomplished. In addition, the linear transformation of polarization multiplexing can be achieved by using the polarization properties of light in diffractive networks instead of being based on birefringence or polarization-sensitive materials, 50 which fully demonstrates the classification and computational potential of diffractive networks in complex-valued matrix vector operations.…”

Combined simulation and experimental study on spectral absorbance of partially disordered MoSe2 nanospheres

“…This structure provides special lighttrapping capability and can be shaped into a thinner absorption layer, which decreases the recombination opportunities of photogenerated electron-hole pairs compared with the bulk material and enhances the charge carriers transfer [4]. Then partially disordered structure enables the generation of a strong light absorption and can be applied in lots of advanced fields, such as ultrathin solar cell, all-optical imaging and photonic crystal [4][5][6].…”