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High resolution three-dimensional imaging with compress sensing

机译：具有压缩感测的高分辨率三维成像

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摘要

LIDAR three-dimensional imaging technology have been used in many fields, such as military detection. However, LIDAR require extremely fast data acquisition speed. This makes the manufacture of detector array for LIDAR system is very difficult. To solve this problem, we consider using compress sensing which can greatly decrease the data acquisition and relax the requirement of a detection device. To use the compressive sensing idea, a spatial light modulator will be used to modulate the pulsed light source. Then a photodetector is used to receive the reflected light. A convex optimization problem is solved to reconstruct the 2D depth map of the object. To improve the resolution in transversal direction, we use multiframe image restoration technology. For each 2D piecewise-planar scene, we move the SLM half-pixel each time. Then the position where the modulated light illuminates will changed accordingly. We repeat moving the SLM to four different directions. Then we can get four low-resolution depth maps with different details of the same plane scene. If we use all of the measurements obtained by the subpixel movements, we can reconstruct a high-resolution depth map of the sense. A linear minimum-mean-square error algorithm is used for the reconstruction. By combining compress sensing and multiframe image restoration technology, we reduce the burden on data analyze and improve the efficiency of detection. More importantly, we obtain high-resolution depth maps of a 3D scene.
机译：LIDAR三维成像技术已被用于许多领域，例如军事检测。但是，激光雷达需要极快的数据采集速度。这使得用于激光雷达系统的探测器阵列的制造非常困难。为了解决这个问题，我们考虑使用压缩感测，它可以大大减少数据采集并放宽对检测设备的要求。为了使用压缩感测思想，将使用空间光调制器来调制脉冲光源。然后，光电探测器用于接收反射光。解决了凸优化问题，以重建对象的2D深度图。为了提高横向分辨率，我们使用了多帧图像恢复技术。对于每个2D分段平面场景，我们每次都将SLM移动半像素。然后，调制光照射的位置将相应改变。我们重复将SLM移至四个不同方向。然后，我们可以获得四个低分辨率深度图，它们具有相同平面场景的不同细节。如果我们使用子像素运动获得的所有测量值，则可以重建该感官的高分辨率深度图。线性最小均方误差算法用于重建。通过将压缩感测和多帧图像恢复技术相结合，我们减轻了数据分析的负担并提高了检测效率。更重要的是，我们获得了3D场景的高分辨率深度图。

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