More articles are being published on lenseless computer vision!
"... Several studies have been done in optical computing to find ways to make electrical neural networks work better. Optical computing has a lot of great benefits, like optical parallelism, which can speed up computing by a lot, and optical passivity, which can lower energy costs and cut down on latency. Optical neural networks (ONNs) are a way to speed up computing and get around the limitations of electrical units’ bandwidth. But ONNs can only work with a coherent laser as a light source, so they can’t be used with a mature machine vision system in scenes with natural light. So, people have come up with hybrid optoelectronic neural networks, in which the front end is optical, and the back end is electrical. These lens-based systems make it harder to use them in edge devices, like self-driving cars.
In a new paper published in Light Science & Application, researchers have developed a lensless optoelectronic neural network (LOEN) architecture for computer vision tasks that uses a passive mask inserted in the imaging light path to perform convolution operations in the optical domain. This reduces the amount of work needed to do calculations and the energy used throughout the whole pipeline. Also, the optical link, image signal processing, and back-end network work together smoothly to achieve joint optimization for specific tasks. ..."
From the abstract:
"Machine vision faces bottlenecks in computing power consumption and large amounts of data. Although opto-electronic hybrid neural networks can provide assistance, they usually have complex structures and are highly dependent on a coherent light source; therefore, they are not suitable for natural lighting environment applications. In this paper, we propose a novel lensless opto-electronic neural network architecture for machine vision applications. The architecture optimizes a passive optical mask by means of a task-oriented neural network design, performs the optical convolution calculation operation using the lensless architecture, and reduces the device size and amount of calculation required. We demonstrate the performance of handwritten digit classification tasks with a multiple-kernel mask in which accuracies of as much as 97.21% were achieved. Furthermore, we optimize a large-kernel mask to perform optical encryption for privacy-protecting face recognition, thereby obtaining the same recognition accuracy performance as no-encryption methods. ..."
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