Abstract
Although conventional microwave imaging techniques offer high-fidelity image reconstructions, they suffer from significant drawbacks, such as intensive hardware complexity. Computational microwave imaging (CMI)-based systems are often considered alternatives to address these challenges, but they are usually limited by the complexity of the computation layer due to the intensive computational demands for the reconstruction step. A variety of deep learning techniques have been proposed to enhance the computation efficiency, but existing studies primarily focus on scenarios where the imaged targets are isolated. To address this challenge, this work explores computational imaging scenarios involving targets of interest overlapping with secondary objects. This article designs a novel generative model to reconstruct the images of the objects in the foreground of overlapping imaging targets. Performance is evaluated using average normalized mean squared error (NMSE) and structural similarity index (SSIM). While conventional methods require manual removal of background objects for accurate foreground reconstructions in overlapped imaging, the proposed method achieves this by learning features directly from the measured signals, significantly improving the CMI computational efficiency.
Original language | English |
---|---|
Title of host publication | Proceedings of the 19th European Conference on Antennas and Propagation, EuCAP 2025 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Number of pages | 5 |
Publication status | Accepted - 31 Dec 2024 |
Event | 19th European Conference on Antennas and Propagation 2025 - Stockholm, Sweden Duration: 30 Mar 2025 → 04 Apr 2025 https://eucap.org/ |
Publication series
Name | EuCAP Proceedings |
---|---|
ISSN (Print) | 2164-3342 |
Conference
Conference | 19th European Conference on Antennas and Propagation 2025 |
---|---|
Abbreviated title | EuCAP 2025 |
Country/Territory | Sweden |
City | Stockholm |
Period | 30/03/2025 → 04/04/2025 |
Internet address |
Keywords
- microwave imaging
- computational microwave imaging
- targets overlapped imaging
- generative learning