Attention-Unet for Electromagnetic Inverse Scattering Problems in Microwave Imaging
Bibliographic record
Abstract
Deep convolutional neural networks (CNNs) are investigated to solve inverse scattering problems for microwave imaging (MWI). The conventional approaches for solving inverse problems encounter challenges such as noisy data and high computational costs. Thus, various deep-learning techniques have been proposed recently to tackle these issues. In this article, the attention-Unet (ATTN-Unet) architecture with attention gates (AGs) is implemented for MWI applications. Further, it is compared against the performance of other CNN-based architectures with similar configurations, namely, DCEDnet, Unet, and Unet-Lite. In addition, the Unet-Lite is implemented with AGs, mainly to evaluate the consistency of performance improvement due to AGs. All the networks have been implemented and tested with complex—real and imaginary—inputs and outputs. The inputs are the backpropagation (BP) of the measured scattered fields onto the imaging domain. The outputs are the reconstructed real and imaginary relative complex permittivity values of an object-of-interest (OI). The results from different networks are compared against each other and against the conventional contrast source inversion (CSI) algorithm. The proposed ATTN-Unet is then tested with experimental data from the University of Manitoba (UM) repository. The results show that the implemented deep-learning method produces image reconstructions of better quality with much lesser computational time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".