Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN
Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN
复制标题
使用轻量级 SR-CNN 进行快速、高质量 3D 太赫兹超分辨率成像
DOI：
10.3390/rs13193800
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发表时间：
2021-10-01
期刊：
影响因子：
5
通讯作者：
Deng, Bin
中科院分区：
文献类型：
--
作者：
Fan, Lei;Zeng, Yang;Deng, Bin
高质量的三维雷达成像是雷达成像增强的挑战性问题之一。现有的稀疏正则化方法存在计算量大、迭代时间长等缺点。与传统的稀疏正则化方法相比，基于卷积神经网络（CNN）的超分辨率（SR）成像方法可以提高成像时间和成像精度。然而，它们仅限于二维空间，并且没有充分考虑小数据集下的模型训练。针对这些问题，提出了一种基于轻量级超分辨率CNN（SR-CNN）的快速高质量三维太赫兹雷达成像方法。首先，给出了一个原始的三维雷达回波模型，并根据给定的成像几何条件推导出期望的SR模型。其次，提出了基于轻量级SR-CNN的SR成像方法，以提高图像质量并加快成像时间。此外，频谱估计，稀疏正则化和SR-CNN之间的分辨率特性进行了分析的点扩展函数（PSF）。最后，电磁计算仿真进行了验证所提出的方法的有效性方面的图像质量。通过烧蚀实验验证了该算法对噪声的鲁棒性和小扰动下的稳定性.
High-quality three-dimensional (3-D) radar imaging is one of the challenging problems in radar imaging enhancement. The existing sparsity regularizations are limited to the heavy computational burden and time-consuming iteration operation. Compared with the conventional sparsity regularizations, the super-resolution (SR) imaging methods based on convolution neural network (CNN) can promote imaging time and achieve more accuracy. However, they are confined to 2-D space and model training under small dataset is not competently considered. To solve these problem, a fast and high-quality 3-D terahertz radar imaging method based on lightweight super-resolution CNN (SR-CNN) is proposed in this paper. First, an original 3-D radar echo model is presented and the expected SR model is derived by the given imaging geometry. Second, the SR imaging method based on lightweight SR-CNN is proposed to improve the image quality and speed up the imaging time. Furthermore, the resolution characteristics among spectrum estimation, sparsity regularization and SR-CNN are analyzed by the point spread function (PSF). Finally, electromagnetic computation simulations are carried out to validate the effectiveness of the proposed method in terms of image quality. The robustness against noise and the stability under small are demonstrate by ablation experiments.