Comparison of U-Net and OASRN Neural Network for Microwave Imaging
學年 111
學期 1
出版(發表)日期 2022-08-23
作品名稱 Comparison of U-Net and OASRN Neural Network for Microwave Imaging
著者 C. C. Chiu; T. H. Kang; P. H. Chen; J. Hao; Y. K. Chen
著錄名稱、卷期、頁數 Journal of Electromagnetic Waves and Applications 37(1), p.93-109
摘要 U-Net and Object-Attentional Super-Resolution Network (OASRN) neural network for electromagnetic imaging are compared and investigated in this paper. The outcome shows that though under limited training data, the regeneration capability is still highly reliable. We first transmit the electromagnetic waves to the scatterer and use the received scattered field information to calculate the estimated permittivity distribution by Green’s function, subspace method and Dominant Current Scheme (DCS). The estimation technique can effectively reduce the training process of the neural network modules. Next, we train the U-Net and OASRN modules for real-time images. Lastly, we used Root Mean Square Error (RMSE) and Structural Similarity Index Measure (SSIM) to compare and analyze the reconstructed images of the two neural networks. Numerical results show that the reconstructed image by OASRN is better than that by U-net with 5% or 20% Gaussian noise for different dielectric constant distributions.
關鍵字 Microwave imaging;U-Net;Object-Attentional Super-Resolution Network (OASRN);convolution neural network;deep learning
語言 en_US
ISSN 1569-3937;0920-5071
期刊性質 國外
收錄於 SCI EI
通訊作者 C. C. Chiu
國別 GBR
出版型式 ,電子版,紙本