@inproceedings{18eb5b8f19444269908f245097df55a2,
title = "SAR Image Reconstruction of Non-Sparse Scene via Deep NSR-Net",
abstract = "Various imaging methods based on compressed sensing (CS) of synthetic aperture radar (SAR) have been proposed to reduce the sample size of echoes required for the imaging process. The unrolling technique further solves the inefficiency of conventional CS-based methods by mapping them into deep neural networks. However, most of these methods are based on sparsity prior of the scene or its transformation domain, which could be invalid for non-sparse scenes. To address this, we proposed a network utilizing the feature priors of the images instead of sparsity for non-sparse scene reconstruction of SAR, namely NSR-Net. We adopt learnable regularization terms in the CS model. Then the iterative solving process of the model is derived and unrolled into the proposed deep neural network to learn the best regularization terms from data. Simulation experiments verified the effectiveness of NSR-Net in the reconstruction of non-sparse scenes with down-sampled SAR echoes.",
keywords = "NSR-Net, PGD, SAR, deep neural network, feature prior",
author = "Ruili Jiang and Min Li and Hongyang An and Zhongyu Li and Junjie Wu and Jianyu Yang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",
year = "2022",
doi = "10.1109/IGARSS46834.2022.9884772",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "939--942",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
}