(49-5) 14 * << * >> * Russian * English * Content * All Issues

Study of image reconstruction efficiency in a single-pixel imaging method using generative adversarial networks
D.V. Babukhin 1, A.A. Reutov 1, D.V. Sych 1

1 P.N. Lebedev Physical Institute, RAS,
53 Leninskiy Prospekt, Moscow, 119991, Russia

 PDF, 2338 kB

DOI: 10.18287/2412-6179-CO-1526

Pages: 818-825.

Full text of article: Russian language.

Abstract:
Single-pixel imaging is a promising image acquisition method that provides an alternative to traditional imaging methods using multi-pixel matrices. However, algorithmic image reconstruction from measurements of a single-pixel camera is a non-trivial computational task that can be solved by machine learning methods. In this work, we investigate the possibility of image reconstruction in the single-pixel imaging method using generative adversarial neural networks. Using computer simulation of a single-pixel camera, we study the efficiency of image reconstruction using two generative network architectures – a deep convolutional generative adversarial network and a generative least squares adversarial network. We find that the generative least squares adversarial network demonstrates a better image reconstruction quality compared to the deep convolutional generative adversarial network. However, when taking into account optical distortions, the deep convolutional adversarial network is more stable in learning to a higher quality compared to the generative least squares adversarial network. The results obtained in this work may serve as a basis for the development of software for the practical application of a single-pixel camera.

Keywords:
single-pixel imaging, image restoration, generative adversarial networks, hardware distortion correction.

Citation:
Babukhin DV, Reutov AA, Sych DV. Study of image reconstruction efficiency in single-pixel imaging method using generative adversarial networks. Computer Optics 2025; 49(5): 818-825. DOI: 10.18287/2412-6179-CO-1526.

Acknowledgements:
This work was supported by the Russian Science Foundation under project No. 23-22-00381, https://rscf.ru/project/23-22-00381/.

References:
  1. Duarte MF, Davenport MA, Takhar D, Laska JN, Sun T, Kelly KF, Baraniuk RG. Single-pixel imaging via compressive sampling. IEEE Signal Process Mag 2008; 25(2): 83-91. DOI: 10.1109/MSP.2007.914730.
  2. Aksenov MD, Sych DV. Optimal data acquisition methods for single-pixel imaging. J Russ Laser Res 2018; 39: 492-498. DOI: 10.1007/s10946-018-9744-y.
  3. Sych D, Aksenov M. Computational imaging with a single-pixel detector and a consumer video projector. AIP Conf Proc 2018; 1936(1): 020016. DOI: 10.1063/1.5025454.
  4. Shcherbatenko M, Elezov M, Manova N, Sedykh K, Korneev A, Korneeva Yu, Dryazgov M, Simonov N, Feimov A, Goltsman G, Sych D. Single-pixel camera with a large-area microstrip superconducting single photon detector on a multimode fiber. Appl Phys Lett 2021; 118(18), 181103. DOI: 10.1063/5.0046049.
  5. Bian L, Suo J, Situ G, Li Z, Fan J, Chen F, Dai Q. Multispectral imaging using a single bucket detector. Sci Rep 2016; 6: 24752. DOI: 10.1038/srep24752.
  6. Zhang Z, Liu S, Peng J, Yao M, Zheng G, Zhong J. Simultaneous spatial, spectral, and 3D compressive imaging via efficient Fourier single-pixel measurements. Optica 2018; 5(3): 315-319. DOI: 10.1364/OPTICA.5.000315.
  7. Zhang Y, Edgar MP, Sun B, Radwell N, Gibson GM, Padgett MJ. 3D single-pixel video. J Opt 2016; 18(3): 035203. DOI: 10.1088/2040-8978/18/3/035203.
  8. Greenberg J, Krishnamurthy K, Brady D. Compressive single-pixel snapshot x-ray diffraction imaging. Opt Lett 2013; 39(1): 111-114. DOI: 10.1364/OL.39.000111.
  9. Olbinado MP, Paganin DM, Cheng Y, Rack A. X-ray phase-contrast ghost imaging using a single-pixel camera. Optica 2021; 8(12): 1538-1544. DOI: 10.1364/OPTICA.437481.
  10. Lyu M, Wang W, Wang H, Wang H, Li G, Chen N, Situ G. Deep-learning-based ghost imaging. Sci Rep 2017; 7: 17865. DOI: 10.1038/s41598-017-18171-7.
  11. He Y, Wang G, Dong G, Zhu S, Chen H, Zhang A, Xu Z. Ghost imaging based on deep learning. Sci Rep 2018; 8: 6469. DOI: 10.1038/s41598-018-24731-2.
  12. Shimobaba T, Endo Y, Nishitsuji T, Takahashi T, Nagahama Y, Hasegawa S, Sano M, Hirayama R, Kakue T, Shiraki A, Ito T. Computational ghost imaging using deep learning. Opt Commun 2018; 413: 147-151. DOI: 10.1016/j.optcom.2017.12.041.
  13. Zhang Z, Li X, Zheng S, Yao M, Zheng G, Zhong J. Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection. Opt Express 2020; 28(9): 13269-13278. DOI: 10.1364/OE.392370.
  14. Higham CF, Murray-Smith R, Padgett MJ, Edgar MP. Deep learning for real-time single-pixel video. Sci Rep 2018; 8: 2369. DOI: 10.1038/s41598-018-20521-y.
  15. Radwell N, Johnson SD, Edgar MP, Higham CF, Murray-Smith R, Padgett MJ. Deep learning optimized single-pixel LiDAR. Appl Phys Lett 2019; 115(23): 231101. DOI: 10.1063/1.5128621.
  16. Zhao M, Li F, Huo F, Tian Z. Generative adversarial network-based single-pixel imaging. Soc for Inf Disp 2022; 30(8): 648-656. DOI: 10.1002/jsid.1128.
  17. Sun S, Yan Q, Zheng Y, Wei Z, Lin J, Cai Y. Single pixel imaging based on generative adversarial network optimized with multiple prior information. IEEE Photonics J 2022; 14(4): 8538110. DOI: 10.1109/JPHOT.2022.3184947.
  18. Lu Y, Tan B, Ding S, Li Y. SPI-CGAN: Single-pixel imaging based on conditional generative adversarial network. J Phys: Conf Ser 2022; 2216: 012092. DOI: 10.1088/1742-6596/2216/1/012092.
  19. Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv Preprint. 2015. Source: <https://arxiv.org/abs/1511.06434>. DOI: 10.48550/arXiv.1511.06434.
  20. Mao X, et al. Least squares generative adversarial networks. 2017 IEEE Int Conf on Computer Vision (ICCV)2017: 2813-2821. DOI: 10.1109/ICCV.2017.304.
  21. Ledig C, et al. Photo-realistic single image super-resolution using a generative adversarial network. 2017 IEEE Conf on Computer Vision and Pattern Recognition (CVPR) 2017: 105-114. DOI: 10.1109/CVPR.2017.19.
  22. Sych D. Optimization of compressed sampling in single-pixel imaging. Bull Lebedev Phys Inst. 23024; 51: 202-205. DOI: 10.3103/S1068335624600463.
  23. Reutov AA, Babukhin DV, Sych DV. Object classification using a single-pixel camera and neural networks. Computer Optics 2025; 49(3): 517-524. DOI: 10.18287/2412-6179-CO-1538.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20