红外与激光工程, 2020, 49(S2): 20200380, 网络出版: 2020-01-01

基于深度学习的压缩光子计数激光雷达

Compressed photon-counting laser radar based on deep learning
作者单位

南昌大学 信息工程学院,江西 南昌 330031

摘要
压缩光子计数雷达是光子计数激光雷达技术与单像素成像技术的结合,具有低成本、超高灵敏度等优点,但在进行高分辨率成像时,需要大量的测量和迭代计算进行重建,导致所需成像时间很长。当前的研究热点深度学习压缩重建网络被证明可避免迭代运算实现快速压缩测量重建,但已有文献报道的深度学习压缩重建网络,采用传统的图像处理数据库的无噪声图片或在图片上加高斯噪声进行训练网络,网络应用于实际的压缩光子计数雷达系统,性能有待进一步验证。自主设计了基于FPGA的同步控制测量模块,搭建出压缩光子计数雷达系统,提出了基于蒙特卡洛模拟压缩光子计数雷达系统的方法来制作训练数据,并设计深度学习压缩重建网络DFC-Net进行采样和重建联合优化。实验结果表明:在10%、15%、20%、25%、30%采样率下,DFC-Net重建性能优于现有的重建网络Dr2-Net和传统的压缩重建算法TVAL3。
Abstract
Compressed photon-counting radar is a combination of photon-counting radar technology and single-pixel imaging technology. It has the advantages of low cost and ultra-high sensitivity. However, the reconstruction of high-resolution imaging cost a large number of measurements and iterative calculations, resulting in a long imaging time. At present, deep learning compression reconstruction network has been proved to be able to avoid iterative computation and achieve rapid compression measurement reconstruction, But the deep learning compression reconstruction network reported in the literature uses traditional image processing database of noise-free pictures or adding Gaussian noise to the pictures to train the network, the network is applied to the actual compressed photon counting radar system, the performance needs to be further verified. A synchronous control measurement module based on FPGA was independently designed, a compressed photon-counting radar system was built, a Monte Carlo simulation of compressed photon-counting radar system to produce training data was proposed, and a deep learning compression reconstruction network DFC-Net was designed for joint optimization of sampling and reconstruction. The experimental results show that: at 10%, 15%, 20%, 25%, and 30% sampling rate, the reconstruction performance of DFC-Net is better than the existing reconstruction network Dr2-Net and the traditional compression reconstruction algorithm TVAL3.
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