This paper presents a novel compressed sensing (CS) algorithm and camera design for light field video capture using a single sensor consumer camera module. Unlike microlens light field cameras which sacrifice spatial resolution to obtain angular information, our CS approach is designed for capturing light field videos with high angular, spatial, and temporal resolution. The compressive measurements required by CS are obtained using a random color‐coded mask placed between the sensor and aperture planes. The convolution of the incoming light rays from different angles with the mask results in a single image on the sensor; hence, achieving a significant reduction on the required bandwidth for capturing light field videos. We propose to change the random pattern on the spectral mask between each consecutive frame in a video sequence and extracting spatio‐angular‐spectral‐temporal 6D patches. Our CS reconstruction algorithm for light field videos recovers each frame while taking into account the neighboring frames to achieve significantly higher reconstruction quality with reduced temporal incoherencies, as compared with previous methods. Moreover, a thorough analysis of various sensing models for compressive light field video acquisition is conducted to highlight the advantages of our method. The results show a clear advantage of our method for monochrome sensors, as well as sensors with color filter arrays.

中文翻译：

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单传感器压缩光场摄像机

本文介绍了一种新颖的压缩传感 (CS) 算法和相机设计，用于使用单个传感器消费类相机模块进行光场视频捕获。与牺牲空间分辨率来获取角度信息的微透镜光场相机不同，我们的 CS 方法旨在捕获具有高角度、空间和时间分辨率的光场视频。CS 所需的压缩测量是使用放置在传感器和孔径平面之间的随机颜色编码掩模获得的。来自不同角度的入射光线与遮罩的卷积在传感器上产生单个图像；因此，显着减少了捕获光场视频所需的带宽。我们建议在视频序列中的每个连续帧之间改变光谱掩码上的随机模式，并提取空间-角度-光谱-时间 6D 块。与以前的方法相比，我们的光场视频 CS 重建算法在考虑相邻帧的同时恢复每一帧，以实现更高的重建质量，同时减少时间不连贯性。此外，对压缩光场视频采集的各种传感模型进行了彻底分析，以突出我们方法的优势。结果显示了我们的方法对于单色传感器以及带有滤色器阵列的传感器的明显优势。与以前的方法相比，我们的光场视频 CS 重建算法在考虑相邻帧的同时恢复每一帧，以实现更高的重建质量，同时减少时间不连贯性。此外，对压缩光场视频采集的各种传感模型进行了彻底分析，以突出我们方法的优势。结果显示了我们的方法对于单色传感器以及带有滤色器阵列的传感器的明显优势。与以前的方法相比，我们的光场视频 CS 重建算法在考虑相邻帧的同时恢复每一帧，以实现更高的重建质量，同时减少时间不连贯性。此外，对压缩光场视频采集的各种传感模型进行了彻底分析，以突出我们方法的优势。结果显示了我们的方法对于单色传感器以及带有滤色器阵列的传感器的明显优势。