Deconvolving Diffraction for Fast Imaging of Sparse Scenes
Deconvolving Diffraction for Fast Imaging of Sparse Scenes
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DOI：
10.1109/iccp51581.2021.9466266
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发表时间：
2021-05
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影响因子：
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通讯作者：
Mark Sheinin;Matthew O'Toole;S. Narasimhan
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作者：
Mark Sheinin;Matthew O'Toole;S. Narasimhan
大多数计算机视觉技术依赖于均匀采样2D图像平面的相机。然而，存在一类应用，对于这些应用，图像平面的标准均匀2D采样是次优的。这类应用包括感兴趣的场景点稀疏地占据图像平面的应用（例如，基于标记的运动捕捉），并且因此2D相机传感器的大多数像素将被浪费。最近，衍射光学器件与稀疏（例如，线）传感器来实现这种稀疏场景的高速捕获。一种称为“衍射线成像”的这样的方法依赖于使用衍射光栅来将场景点的点扩展函数（PSF）从点扩展到颜色编码的形状（例如，水平线），其与线传感器的相交使得能够进行点定位。在本文中，我们扩展了这种方法的任意衍射光学元件和任意采样的传感器平面使用卷积为基础的图像形成模型。然后通过制定卷积编码逆问题来恢复稀疏场景，该逆问题可以在不使用多个传感器的情况下解决衍射PSF的混合物，将基于衍射的成像的应用扩展到一类新的显著密集的场景。对于单轴衍射光栅的情况下，我们提供了一种方法来确定精确的场景恢复所需的最小传感器子采样。与使用来自窄带源的散斑PSF或具有滚动快门传感器的基于漫射器的PSF的方法相比，我们的方法使用来自宽带源的光谱编码的PSF，并且分别允许任意传感器采样。我们证明，所提出的组合的成像方法和场景恢复方法是非常适合于高速标记为基础的运动捕捉和粒子图像测速（PIV）在很长一段时间。
Most computer vision techniques rely on cameras which uniformly sample the 2D image plane. However, there exists a class of applications for which the standard uniform 2D sampling of the image plane is sub-optimal. This class consists of applications where the scene points of interest occupy the image plane sparsely (e.g., marker-based motion capture), and thus most pixels of the 2D camera sensor would be wasted. Recently, diffractive optics were used in conjunction with sparse (e.g., line) sensors to achieve high-speed capture of such sparse scenes. One such approach, called “Diffraction Line Imaging”, relies on the use of diffraction gratings to spread the point-spread-function (PSF) of scene points from a point to a color-coded shape (e.g., a horizontal line) whose intersection with a line sensor enables point positioning. In this paper, we extend this approach for arbitrary diffractive optical elements and arbitrary sampling of the sensor plane using a convolution-based image formation model. Sparse scenes are then recovered by formulating a convolutional coding inverse problem that can resolve mixtures of diffraction PSFs without the use of multiple sensors, extending the application of diffraction-based imaging to a new class of significantly denser scenes. For the case of a single-axis diffraction grating, we provide an approach to determine the minimal required sensor sub-sampling for accurate scene recovery. Compared to methods that use a speckle PSF from a narrow-band source or a diffuser-based PSF with a rolling shutter sensor, our approach uses spectrally-coded PSFs from broad-band sources and allows arbitrary sensor sampling, respectively. We demonstrate that the presented combination of the imaging approach and scene recovery method is well suited for high-speed marker based motion capture and particle image velocimetry (PIV) over long periods.