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Resource-Saving and High-Robustness Image Sensing Based on Binary Optical Computing
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DOI：10.1002/lpor.202400936.png)
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Abstract
En 中文
Computational imaging, as a novel technology utilizing encoded image acquisition, relies on intelligent decoding methods for effective image restoration and sensing. Optical computing-based decoders can efficiently process and extract features from pre-sensor information, reducing the computational burden on digital computers. However, mainstream parallel optical neural network (ONN) architectures based on wavefront propagation typically possess complex network structures and high-precision parameters, which pose challenges in terms of precise fabrication and system calibration, as well as sensitivity to signal-to-noise ratios. In this work, a binary-weighted optical computing engine is proposed with spatial multiplexing and aggregation (B-OSMA), a large-scale passive ONN implementation that achieves high-efficiency image sensing. Employing B-OSMA as an optical decoder, demonstrated image categorizing from 2% compressive is experimented sampling with 92.0% and 83.8% accuracy on MNIST and fashion-MNIST datasets, respectively, approaching the performance of full-precision electronic computing while reducing storage requirements by 97%. Compared to conventional ONNs with analog weights, the B-OSMA exhibits enhanced resilience against systematic errors and ambient noise. This work represents a significant advancement towards practical applications of optical computing in image sensing. A novel binary-weighted optical computing implementation with spatial multiplexing and aggregation (B-OSMA) scheme achieves high-efficiency image sensing. Coupling with a light-weighted digital discriminator, B-OSMA demonstrates good performances that are competitive to full-precision electric computing on MNIST and Fashion-MNIST datasets, with greatly reduced storage consumption and promoted resilience against systematic errors and ambient noise. image
Keywords:
binary neural network
compressive sensing
image sensing
optical computing
optical neural network