Advanced Imaging, Volume. 2, Issue 3, 031003(2025)
Compressive single-pixel imaging with low-order nonlinear neural networks
Fig. 1. Schematic of the experimental setup and reconstruction principle. (a) Schematic diagram of the experimental setup. An incoherent light source illuminates the object, which is then imaged onto the DMD surface with L1. Once modulated by patterns loaded on the DMD, the total intensity of the modulated light field is captured by a single-pixel detector. (b) The first fully connected layer that corresponds to the physical process of SPI. (c) The 1st-order PNN structure is chosen as the reconstruction algorithm of two linear tasks: compressive single-pixel imaging and linear-edge extraction. (d), (e) Simulation results of two linear tasks with a sampling ratio of 6.25%. (f) The 2nd-order PNN is selected as the reconstruction algorithm of two nonlinear tasks: nonlinear-edge extraction and handwritten dataset classification; the symbol
Fig. 2. Experimental results of three regression tasks. For each task, images in the first row represent the experimental results, and the second-row images are the full-sampling results of Hadamard patterns undergoing respective mathematical processing defined in the
Fig. 3. Experimental results of classification task. The confusion matrix of handwritten dataset classification has an accuracy rate of 99%.
Fig. 4. Quantitative assessment of the robustness of different-order PNNs with four CSPI tasks and
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Huaijian Chen, Xiao Wang, Botao Hu, Aiping Fang, Ruifeng Liu, Pei Zhang, Fuli Li, "Compressive single-pixel imaging with low-order nonlinear neural networks," Adv. Imaging 2, 031003 (2025)
Category: Research Article
Received: Apr. 7, 2025
Accepted: May. 29, 2025
Published Online: Jun. 30, 2025
The Author Email: Ruifeng Liu (ruifeng.liu@mail.xjtu.edu.cn), Pei Zhang (zhang.pei@mail.xjtu.edu.cn)