A Trained Iterative Shrinkage Approach Based on Born Iterative Method for Electromagnetic Imaging

A trained-based Born iterative method (TBIM) is developed for electromagnetic imaging (EMI) applications. The proposed TBIM consists of a nested loop; the outer loop executes TBIM iteration steps, while the inner loop executes a trained iterative shrinkage thresholding algorithm (TISTA). The applied...

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Vydáno v:IEEE transactions on microwave theory and techniques Ročník 70; číslo 11; s. 1 - 9
Hlavní autor: Desmal, Abdulla
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Electromagnetic imaging (EMI)
Electromagnetic interference
Image quality
Image reconstruction
Image restoration
Imaging
Inverse problems
Inverse scattering
Iterative methods
Linear operators
machine learning (ML)
Memory management
Nested loops
nonlinear optimization
Permittivity
Regularization
Resource management
sparse regularization
Training
ISSN:0018-9480, 1557-9670
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Shrnutí:A trained-based Born iterative method (TBIM) is developed for electromagnetic imaging (EMI) applications. The proposed TBIM consists of a nested loop; the outer loop executes TBIM iteration steps, while the inner loop executes a trained iterative shrinkage thresholding algorithm (TISTA). The applied TISTA runs linear Landweber iterations implemented with a trained regularization network designed based on U-net architecture. A normalization process was imposed on the linear operator to make the linear inverse problem in TBIM consistent with TISTA assumptions. The iterative utilization of the regularization network in TISTA is a bottleneck that demands high memory allocation through the training process. Therefore, TISTA within each TBIM step was trained separately. The TISTA regularization network in each TBIM step was initialized using the weights from the previous TBIM step. The abovementioned approach achieved high-quality image restoration after running a few TBIM steps while maintaining low memory allocation through the training process. The numerical results illustrated show the superiority of the proposed TBIM compared with another three inverse scattering schemes. In addition, verification of experimental results based on the Fresnel institute database is demonstrated.
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ISSN:0018-9480
1557-9670
DOI:10.1109/TMTT.2022.3205650