Weighted sampling-adaptive single-pixel sensing

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2838-2841
Number of pages4
JournalOptics Letters
Volume47
Issue number11
DOIs
Publication statusPublished - 1 Jun 2022

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