Generative model-based image reconstruction from back-scattered data in computational microwave imaging

Jiaming Zhang; Cien Zhang; Rahul Sharma; Peiao Li; Jie Zhang; Okan Yurduseven

doi:10.1117/12.3032963

15 November 2024 Generative model-based image reconstruction from back-scattered data in computational microwave imaging

Jiaming Zhang, Cien Zhang, Rahul Sharma, Peiao Li, Jie Zhang, Okan Yurduseven

Author Affiliations +

Proceedings Volume 13203, Sensors and Communication Technologies in the 1 GHz to 10 THz Band; 1320305 (2024) https://doi.org/10.1117/12.3032963
Event: Security + Defence, 2024, Edinburgh, United Kingdom

Abstract

Conventional microwave imaging can provide high-quality reconstructed images, but is also limited by the increased hardware complexity and a slow data acquisition speed. Although computational imaging (CI)-based systems are developed to be alternatives, they may require substantial computational power and time. To reduce the hardware complexity and computational burden associated with scene reconstructions of CI applications, in this paper, a conditional generative adversarial network (cGAN) is presented to achieve image reconstruction, where the back-scattered measurement is regarded as both the condition and the input of the proposed network. With testing dataset, the average values of the normalized mean squared error (NMSE) and the normalized mean absolute error (NMAE) are 0.0474 and 0.2267, respectively. In addition, a noise analysis is conducted, showing the reliability of the proposed network in noisy settings.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiaming Zhang, Cien Zhang, Rahul Sharma, Peiao Li, Jie Zhang, and Okan Yurduseven "Generative model-based image reconstruction from back-scattered data in computational microwave imaging", Proc. SPIE 13203, Sensors and Communication Technologies in the 1 GHz to 10 THz Band, 1320305 (15 November 2024); https://doi.org/10.1117/12.3032963

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE