Phys.org March 29, 2021
To mitigate the shortcomings and inefficiencies of traditional machine vision systems researchers at UCLA leveraged deep learning to design optical networks created by successive diffractive surfaces to perform computation and statistical inference as the input light passes through specially designed and 3D-fabricated layers. The diffractive optical networks are designed to process the incoming light at selected wavelengths with the goal of extracting and encoding the spatial features of an input object onto the spectrum of the diffracted light, which is collected by a single-pixel detector. Different object types or classes of data are assigned to different wavelengths of light. The input objects are automatically classified optically, merely using the output spectrum detected by a single pixel, bypassing the need for an image sensor-array or a digital processor. They demonstrated the success of their framework at terahertz wavelengths by classifying the images of handwritten digits using a single pixel detector and 3D printed diffractive layers. The work provides transformative advantages in terms of frame rate, memory requirement and power efficiency, which are important for mobile computing applications…read more. Open Access TECHNICAL ARTICLEÂ
Schematics of spectral encoding of spatial information for object classification and image reconstruction. Credit: Science Advances 26 Mar 2021: Vol. 7, no. 13, eabd7690Â