Have a personal or library account? Click to login
RESE-CNN: Residual Squeeze-and-Excitation Network for High-Contrast Optical Tomography Reconstruction Cover

RESE-CNN: Residual Squeeze-and-Excitation Network for High-Contrast Optical Tomography Reconstruction

Open Access
|Jun 2025

References

  1. Schleicher, E., Da Silva, M., Thiele, S., Li, A., Wollrab, E., Hampel, U. (2008). Design of an optical tomograph for the investigation of single- and two-phase pipe flows. Measurement Science and Technology, 19(9), 094006. https://doi.org/10.1088/0957-0233/19/9/094006.
  2. Li, H., Liu, G., Yu, S., Sun, C. (2022). Configuration optimization of optical tomography based on fan-beam lasers. IEEE Transactions on Instrumentation and Measurement, 71, 4505810. https://doi.org/10.1109/TIM.2022.3201238.
  3. Arridge, S. R. (1999). Optical tomography in medical imaging. Inverse problems. 15(2), R41. https://doi.org/10.1088/0266-5611/15/2/022.
  4. Cui, Z., Wang, Q., Xue, Q., Fan, W., Zhang, L., Cao, Z., Sun, B., Wang, H., Yang, W. (2016). A review on image reconstruction algorithms for electrical capacitance/resistance tomography. Sensor Review, 36(4), 429–445. https://doi.org/10.1108/SR-01-2016-0027.
  5. Beck, M., Dyakowski, T., Williams, R. (1998). Process tomography-the state of the art. Transactions of the Institute of Measurement and Control, 20(4), 163–177. https://doi.org/10.1177/014233129802000402.
  6. Yang, W., Peng, L. (2002). Image reconstruction algorithms for electrical capacitance tomography. Measurement Science and Technology, 14(1), R1. https://doi.org/10.1088/0957-0233/14/1/201.
  7. Khairi, M. T. M., Ibrahim, S., Yunus, M. A. M., Faramarzia, M., Ayub, N. M. N. (2015). Application of optical tomography for monitoring gas bubbles flow based on independent component analysis algorithm. Jurnal Teknologi, 73(3), 145–152. https://doi.org/10.11113/jt.v73.4260.
  8. Ben Yedder, H., Cardoen, B., Shokoufi, M., Golnaraghi, F., Hamarneh, G. (2022). Multitask deep learning reconstruction and localization of lesions in limited angle diffuse optical tomography. IEEE Transactions on Medical Imaging, 41(3), 515–530. https://doi.org/10.1109/TMI.2021.3117276.
  9. Lecun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539.
  10. Deng, A., Huang, J., Liu, H., Cai, W. (2020). Deep learning algorithms for temperature field reconstruction of nonlinear tomographic absorption spectroscopy. Measurement: Sensors, 10–12, 100024. https://doi.org/10.1016/j.measen.2020.100024.
  11. Krizhevsky, A., Sutskever, I., Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386.
  12. Badrinarayanan, V., Kendall, A., Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615.
  13. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q. (2017). Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 4700–4708. https://doi.org/10.1088/0957-0233/19/9/094006.
  14. Wu, Y., Chen, B., Liu, K., Zhu, C., Pan, H., Jia, J., Wu, H., Yao, J. (2021). Shape reconstruction with multi-phase conductivity for electrical impedance tomography using improved convolutional neural network method. IEEE Sensors Journal, 21(7), 9277–9287. https://doi.org/10.1109/JSEN.2021.3050845.
  15. Huang, J., Liu, H., Dai, J., Cai, W. (2018). Reconstruction for limited-data nonlinear tomographic absorption spectroscopy via deep learning. Journal of Quantitative Spectroscopy and Radiative Transfer, 218, 187–193. https://doi.org/10.1016/j.jqsrt.2018.07.011.
  16. He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 770–778. https://doi.org/10.1109/CVPR.2016.90.
  17. Hu, J., Shen, L., Sun, G. (2018). Squeeze-and-excitation networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). IEEE, 7132–7141. https://doi.org/10.1109/CVPR.2018.00745.
  18. Fukui, H., Hirakawa, T., Yamashita, T., Fujiyoshi, H. (2019). Attention branch network: Learning of attention mechanism for visual explanation. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 10705–10714. https://doi.org/10.1109/CVPR.2019.01096.
  19. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., Polosukhin, I. (2017). Attention is all you need. In NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems. New York, US: Curran Associates, 6000–6010. ISBN 978–1–5108–6096–4.
  20. Itti, L., Koch, C. (2001). Computational modelling of visual attention. Nature Reviews Neuroscience, 2(3), 194–203. https://doi.org/10.1038/35058500.
  21. Olshausen, B. A., Anderson, C. H., Van Essen, D. C. (1993). A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. Journal of Neuroscience, 13(11), 4700–4719. https://doi.org/10.1523/JNEUROSCI.13-11-04700.1993.
  22. Yun, J. (2024). Mitigating gradient overlap in deep residual networks with gradient normalization for improved non-convex optimization. In 2024 IEEE International Conference on Big Data (BigData). 3831–3837. https://doi.ieeecomputersociety.org/10.1109/BigData62323.2024.10825094.
  23. Hayat, M. (2024). Squeeze & excitation joint with combined channel and spatial attention for pathology image super-resolution. Franklin Open 8, 100170. https://doi.org/10.1016/j.fraope.2024.100170.
  24. Cai, W., Kaminski, C. F. (2017). Tomographic absorption spectroscopy for the study of gas dynamics and reactive flows. Progress in Energy and Combustion Science, 59, 1–31. https://doi.org/10.1016/j.pecs.2016.11.002.
  25. Chen, Y., Li, K., Han, Y. (2020). Electrical resistance tomography with conditional generative adversarial networks. Measurement Science and Technology, 31(5), 055401. https://doi.org/10.1088/1361-6501/ab62c4.
  26. Li, H., Yu, S., Liu, G., Zheng, X., Sun, C. (2022). Development of optical tomography system based on fan-beam lasers. IEEE Sensors Journal, 22(19), 18718–18725. https://doi.org/10.1109/JSEN.2022.3198493.
  27. Ronneberger, O., Fischer, P., Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. 234–241. https://doi.org/10.1007/978-3-319-24574-4_28.
  28. Guan, S., Khan, A. A., Sikdar, S., Chitnis, P. V. (2020). Fully dense unet for 2-D sparse photoacoustic tomography artifact removal. IEEE Journal of Biomedical and Health Informatics, 24(2), 568–576. https://doi.org/10.1109/JBHI.2019.2912935.
  29. Jha, D., Riegler, M. A., Johansen, D., Halvorsen, P., Johansen, H. D. (2020). Double U-Net: A deep convolutional neural network for medical image segmentation. In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 558–564. https://doi.ieeecomputersociety.org/10.1109/CBMS49503.2020.00111.
  30. Valanarasu, J. M. J., Sindagi, V. A., Hacihaliloglu, I., Patel, V. M. (2021). KiU-Net: Overcomplete convolutional architectures for biomedical image and volumetric segmentation. IEEE Transactions on Medical Imaging, 41(4), 965–976. https://doi.org/10.1109/TMI.2021.3130469.
Language: English
Page range: 72 - 82
Submitted on: Jun 20, 2024
|
Accepted on: Apr 23, 2025
|
Published on: Jun 7, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2025 Yingkuang Zhu, Zhenhua Pan, Huajun Li, Jianyang Chen, Yihao Sheng, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.