DipSAR

Deep Image Prior for Sparse Sampled Near-Field SAR Millimeter-Wave Imaging

Conference paper (2023)

Authors

Rawin Assabumrungrat Tohoku University
N. Kumchaiseemak , Vidyasirimedhi Institute of Science and Technology
Phoom Punpeng Ruamrudee International School
Theerawit Wilaiprasitporn Vidyasirimedhi Institute of Science and Technology
Copyright: © 2023 Rawin Assabumrungrat, N. Kumchaiseemak, J. Wang, D. Wang, Phoom Punpeng, F. Fioranelli, Theerawit Wilaiprasitporn
DOI: https://doi.org/10.1109/SENSORS56945.2023.10325198
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Published Date

2023

Language

English

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Abstract

We present a deep learning-based approach called DipSAR for reconstructing millimeter-wave synthetic aperture radar (SAR) images from sparse samples. The primary challenge lies in the requirement of a large training dataset for deep learning schemes. To overcome this issue, we employ the deep image prior (DIP) technique, which eliminates the need for a large dataset and instead utilizes only the sparse sample itself. Our proposed DipSAR model recovers missing samples from sparse data and reconstructs the SAR image using a conventional method. In this study, we utilize an existing SAR dataset and create fourteen different patterns to generate additional sparse samples by removing certain data points. We then evaluate the performance of DipSAR in comparison to the conventional method. The results show that DipSAR outperforms the conventional method in terms of the intersection over union (IoU) score.

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