Weighted sampling-adaptive single-pixel sensing

Xinrui Zhan, Chunli Zhu, Jinli Suo, Liheng Bian*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

The novel single-pixel sensing technique that uses an end-to-end neural network for joint optimization achieves high-level semantic sensing, which is effective but computation-consuming for varied sampling rates. In this Letter, we report a weighted optimization technique for sampling-adaptive single-pixel sensing, which only needs to train the network once for any dynamic sampling rate. Specifically, we innovatively introduce a weighting scheme in the encoding process to characterize different patterns’ modulation efficiencies, in which the modulation patterns and their corresponding weights are updated iteratively. The optimal pattern series with the highest weights is employed for light modulation in the experimental implementation, thus achieving highly efficient sensing. Experiments validated that once the network is trained with a sampling rate of 1, the single-target classification accuracy reaches up to 95.00% at a sampling rate of 0.03 on the MNIST dataset and 90.20% at a sampling rate of 0.07 on the CCPD dataset for multi-target sensing.

源语言英语
页(从-至)2838-2841
页数4
期刊Optics Letters
47
11
DOI
出版状态已出版 - 1 6月 2022

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