Deep Learning-based Resolution Enhancement in SAR Image for Automotive Radar Sensors
Sung-wook Kang, Hahng-Jun Cho, Hojung Lee, Seongwook Lee · 2023
Forming high-resolution synthetic aperture radar (SAR) images requires large amounts of sampled data, which increases computation time and complexity. Therefore, in this paper, we propose a method to enhance the resolution of SAR images for automotive radar sensors using a generative adversarial network (GAN). The proposed GAN is an unsupervised image-to-image translation GAN based on a variational autoencoder and can form high-resolution SAR images from a small amount of sampled data. The SAR images formed by the proposed method are compared in terms of peak signal-to-noise ratio and structural similarity index measure for performance evaluation, and they are increased by 2.75% and 4.43%, respectively, compared to the existing low-resolution SAR images.