Wang Zhen, Gao Shijie, Sheng Lei
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2020 Dec 18;20(24):7257. doi: 10.3390/s20247257.
The Compressed Sensing (CS) camera can compress images in real time without consuming computing resources. Applying CS theory in the Laser Communication (LC) system can minimize the assumed transmission bandwidth (normally from a satellite to a ground station) and minimize the storage costs of beacon light-spot images; this can save more than ten times the typical bandwidth or storage space. However, the CS compressive process affects the light-spot tracking and key parameters in the images. In this study, we quantitatively explored the feasibility of the CS technique to capture light-spots in LC systems. We redesigned the measurement matrix to adapt to the requirement of light-tracking. We established a succinct structured deep network, the Compressed Sensing Denoising Center Net (CSD-Center Net) for denoising tracking computation from compressed image information. A series of simulations was made to test the performance of information preservation in beacon light spot image storage. With the consideration of CS ratio and application scenarios, coupled with CSD-Center Net and standard centroid, CS can achieve the tracking function well. The information preserved in compressed information correlates with the CS ratio; higher CS ratio can preserve more details. In fact, when the data rate is up than 10%, the accuracy could meet the requirements what we need in most application scenarios.
压缩感知（CS）相机能够实时压缩图像，且不消耗计算资源。将CS理论应用于激光通信（LC）系统，可以将假定的传输带宽（通常是从卫星到地面站）降至最低，并将信标光斑图像的存储成本降至最低；这可以节省超过十倍的典型带宽或存储空间。然而，CS压缩过程会影响图像中的光斑跟踪和关键参数。在本研究中，我们定量探索了CS技术在LC系统中捕获光斑的可行性。我们重新设计了测量矩阵，以适应光跟踪的要求。我们建立了一个简洁的结构化深度网络，即压缩感知去噪中心网络（CSD-Center Net），用于从压缩图像信息中进行去噪跟踪计算。进行了一系列模拟，以测试信标光斑图像存储中的信息保存性能。考虑到CS比率和应用场景，结合CSD-Center Net和标准质心，CS能够很好地实现跟踪功能。压缩信息中保留的信息与CS比率相关；更高的CS比率可以保留更多细节。事实上，当数据速率高于10%时,精度能够满足我们在大多数应用场景中的需求。
