- March 26, 2021
-- Machine vision systems are used in various applications, including self-driving cars and intelligent manufacturing. Most of these systems utilize lens-based cameras, and after an image or video is captured a digital processor is used to perform machine learning tasks, such as object classification. Such a traditional machine vision architecture suffers from several drawbacks. First, the large amount of digital information makes it hard to achieve image/video analysis at high speed, especially using mobile and battery-powered devices. In addition, the captured images usually contain redundant information, which overwhelms the digital processor with a high computational burden, creating inefficiencies in terms of power and memory requirements. Moreover, beyond the visible wavelengths of light, fabricating high-pixel-count image sensors, such as what we have in our mobile phone cameras, is challenging and expensive, which limits the applications of standard machine vision methods at longer wavelengths, such as terahertz part of the spectrum.
To address these challenges, UCLA researchers have developed a new single-pixel machine vision framework leveraging deep learning to design optical networks created by successive diffractive surfaces to perform computation and statistical inference as the input light passes through these specially designed and 3D-fabricated layers. These 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. This all-optical inference and machine vision capability through a single-pixel detector that is coupled to a diffractive network provides transformative advantages in terms of frame rate, memory requirement and power efficiency, which are especially important for mobile computing applications. Published in Science Advances
, UCLA researchers experimentally 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.
This research was led by Professor Aydogan Ozcan, the Volgenau Chair for Engineering Innovation at the Electrical and Computer Engineering (ECE) department at UCLA, along with Professor Mona Jarrahi, the director of the Terahertz Electronics Laboratory at UCLA. The other authors of this work include graduate students Jingxi Li, Deniz Mengu, Yi Luo, Xurong Li, Muhammed Veli, post-doctoral senior researcher Dr. Nezih T. Yardimci, Adjunct Professor Dr. Yair Rivenson, all with the ECE department at UCLA.Link to paper
: Science Advances
DOI: 10.1126/sciadv.abd7690, https://advances.sciencemag.org/content/7/13/eabd7690