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Research Article

Comparison of U-Net and OASRN neural network for microwave imaging

ORCID Icon, , ORCID Icon, &
Pages 93-109 | Received 31 Mar 2022, Accepted 30 Jul 2022, Published online: 23 Aug 2022

References

  • Chiu CC. Inverse scattering of inhomogeneous biaxial materials coated on a conductor. IEEE Trans Antennas Propag. Feb. 1998;46(2):218–225.
  • Chen X. Subspace-based optimization method for solving inverse scattering problems. IEEE Trans Geosci Remote Sens. Jan. 2010;48(1):42–49.
  • Chiu CC, Yen CY, Lee GX. Dielectric objects reconstruction by combining subspace-based algorithm and randomly global optimixation algorithm. J Electromagn Waves Appl. Jan. 2018;32(1):77–91.
  • Chiu CC, Lee GZ, Jiang H, et al. Microwave imaging of a periodic homogeneous dielectric object buried in rough surfaces. J Electromagn Waves Appl. Aug. 2019;33(14):1905–1919.
  • Wei TF, Wang XH, Wang L, et al. Efficient born iterative method for inverse scattering based on modified forward-solver. IEEE Access. Dec. 2020;8:229101–229107.
  • Caorsi S, Gamba P. Electromagnetic detection of dielectric cylinders by a neural network approach. IEEE Trans Geosci Remote Sens. Mar. 1999;37(2):820–827.
  • Rekanos IT. Neural-network-based inverse-scattering technique for online microwave medical imaging. IEEE Trans Magnetics. Mar. 2002;38(2):1061–1064.
  • Yao HM, Sha WEI, Jiang L. Two-step enhanced deep learning approach for electromagnetic inverse scattering problems. IEEE Antennas Wirel Propaga Lett. Nov. 2019;18(11):2254–2258.
  • Guo R, Song X, Li M, et al. Supervised descent learning technique for 2-D microwave imaging. IEEE Trans Antennas Propag. May 2019;67(5):3550–3554.
  • Chen G, Shah P, Stang J, et al. Learning-assisted multimodality dielectric imaging. IEEE Trans Antennas Propag. Mar. 2019;68(3):2356–2369.
  • Li L, Wang LG, Teixeira FL. Performance analysis and dynamic evolution of deep convolutional neural network for electromagnetic inverse scattering. IEEE Antennas Wireless Propaga Lett. Nov. 2019;18(11):2259–2263.
  • Li L, Wang LG, Teixeira FL, et al. DeepNIS: deep neural network for nonlinear electromagnetic inverse scattering. IEEE Trans Antennas Propag. Mar. 2019;67(3):1819–1825.
  • Wei Z, Chen X. Deep-learning schemes for full-wave nonlinear inverse scattering problems. IEEE Trans Geosci Remote Sens. Apr. 2019;57(4):1849–1860.
  • Wei Z, Chen X. Physics-inspired convolutional neural network for solving full-wave inverse scattering problems. IEEE Trans Antennas Propag. Sept. 2019;67(9):6138–6148.
  • Khoshdel V, Ashraf A, LoVetri J. Enhancement of multimodal microwave-ultrasound breast imaging using a deep-learning technique. Sensors. Sept. 2019;19(18):4050.
  • Xiao J, Li J, Chen Y, et al. Fast electromagnetic inversion of inhomogeneous scatterers embedded in layered media by born approximation and 3-D U-Net. IEEE Trans Geosci Remote Sens. Oct. 2020;17(10):1677–1681.
  • Sanghvi Y, Kalepu Y, Khankhoje UK. Embedding deep learning in inverse scattering problems. IEEE Trans Computa Imag. May. 2020;6:46–56.
  • Zhou H, Ouyang T, Li Y, et al. Linear-model-inspired neural network for electromagnetic inverse scattering. IEEE Antennas Wireless Propaga Lett. Sept. 2020;19(9):1536–1540.
  • Peng P, Jalali S, Yuan X. Solving inverse problems via auto-encoders. IEEE J Sel Areas Informa Theory. Mar. 2020;1(1):312–323.
  • Chen X, Wei Z, Li M, et al. A review of deep learning approaches for inverse scattering problems (invited review). Prog Electromag Research. Jan. 2020;167:67–81.
  • Yao HM, Jiang L, Sha WEI. Enhanced deep learning approach based on the deep convolutional encoder–decoder architecture for electromagnetic inverse scattering problems. IEEE Antennas Wireless Propaga Lett. July 2020;19(7):1211–1215.
  • Shao W, Du Y. Microwave imaging by deep learning network: feasibility and training method. IEEE Tran Antennas Propaga. July 2020;68(7):5626–5635.
  • Ye X, Bai Y, Song R, et al. An inhomogeneous background imaging method based on generative adversarial network. IEEE Trans Microw Theory Tech. Nov. 2020;68(11):4684–4693.
  • Zhou Y, Zhong Y, Wei Z, et al. An improved deep learning scheme for solving 2-D and 3-D inverse scattering problems. IEEE Tran Antennas Propaga. May 2021;69(5):2853–2863.
  • Ma Z, Xu K, Song R, et al. Learning-based fast electromagnetic scattering solver through generative adversarial networks. IEEE Tran Antennas Propaga. Apr. 2021;69(4):2194–2208.
  • Huang Y, Song R, Xu K, et al. Deep learning-based inverse scattering with structural similarity loss functions. IEEE Sens J. Feb. 2021;21(4):4900–4907.
  • Ronneberger FP, Brox T. U-net: convolutional networks for biomedical image segmentation. Proc. 18th Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. (MICCAI); 2015:234–241.
  • Jin KH, McCann MT, Froustey E, et al. Deep convolutional neural network for inverse problems in imaging. IEEE Trans Image Process. Sept. 2017;26(9):4509–4522.
  • Gerazov B, Conceicao RC. Deep learning for tumour classification in homogeneous breast tissue in medical microwave imaging. IEEE eurocon 2017 -17th Interna Conf Smart Tech; Jul. 2017:564–569.
  • Shah P, Moghaddam M. Super resolution for microwave imaging: a deep learning approach. 2017 IEEE Interna Sympo Antennas Propaga Na Radio Scien Meeting; 2017: 849–850.
  • Song Q, Xu F, Jin YQ. Radar image colorization: converting single- polarization to fully polarimetric using deep neural networks. IEEE Access. Dec. 2017;6:1647–1661.
  • Chen H, Zhang Y, Kalra MK, et al. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med ImagJun. 2017;36(12):2524–2535.
  • Yang G, Yu S, Dong H, et al. DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Trans. Med. Imag. Jun 2018;37(6):1310–1321.
  • Chen SW, Tao CS. PolSAR image classification using polarimetric- feature-driven deep convolutional neural network. IEEE Geosci Remote Sens Lett. Apr. 2018;15(4):627–631.
  • Xiao J, Liu Z, Zhao P, et al. Deep learning image reconstruction simulation for electromagnetic tomography. IEEE Sens J. Apr. 2018;18(8):3290–3298.
  • Hamilton SJ, Hauptmann A. Deep D-Bar: real-time electrical impedance tomography imaging with deep neural networks. IEEE Trans Med Imag. Oct. 2018;37(10):2367–2377.
  • Zhang Z, Liang X, Dong X, et al. A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution. IEEE Trans Med Imag. June 2018;37(6):1407–1417.
  • Shan H, Zhang Y, Yang Q, et al. 3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network. IEEE Trans Med Imag. June 2018;37(6):1522–1534.
  • Quan TM, Nguyen-Duc T, Jeong WK. Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans Med Imag. June 2018;37(6):1488–1497.
  • Wang G, Ye JC, Mueller K, et al. Image reconstruction is a new frontier of machine learning. IEEE Trans Med Imag. June 2018;37(6):1289–1296.
  • Zheng J, Peng L. An autoencoder-based image reconstruction for electrical capacitance tomography. IEEE Sens J. Jul. 2018;8(13):5464–5474.
  • Xiao J, Liu Z, Zhao P, et al. Deep learning image reconstruction simulation for electromagnetic tomography. IEEE Sen J. Apr. 2018;18(8):3290–3298.
  • Wei Z, Chen X. Induced-current learning method for nonlinear reconstructions in electrical impedance tomography. IEEE Transactions on Medical Imaging. Oct. 2019;39(5):1326–1334.
  • Wei Z, Liu D, Chen X. Dominant-current deep learning scheme for electrical impedance tomography. IEEE Trans Biomed Eng. Oct. 2019;66(9):2546–2555.
  • Tan C, Lv S, Dong F, et al. Image reconstruction based on convolutional neural network for electrical resistance tomography. IEEE Sens J. Jan. 2019;19(1):196–204.
  • Yaman B, Hosseini SAH, Moeller S, et al. Self-supervised physics-based deep learning MRI reconstruction without fully-sampled data. 2020 IEEE 17th Interna Sympos Biomed Imag; 2020:921–925.
  • Mojabi P, Hughson M, Khoshdel V, et al. CNN for compressibility to permittivity mapping for combined ultrasound-microwave breast imaging. IEEE J Mult Multiph Computa Tech. Apr. 2021;6:62–72.
  • Liu Y, Wang Y, Li N, et al. An attention-based approach for single image super resolution. 2018 24th Interna Conf Patt Recogni; Aug 2018:2777–2784.
  • Sun K, Xiao B, Liu D, et al. Deep high-resolution representation learning for human pose estimation. 2019 IEEE/CVF Conf Compu Vision Patt Recogni; June 2019: 5686–5696.

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