A Study on Compressed Sensing of Ray-Space Data

在本研究中，我们提出了一种新的圆形相机阵列的光线空间压缩传感方法。压缩感知利用了光线空间的特殊特性，即变换后的数据具有稀疏的数据结构。作为第一步，我们定义了一种新的光线空间和新区域之间的变换和逆变换方法，可以有效地减少信息量。我们利用极坐标系中光线空间的特点进行积分变换。我们通过引入逐次逼近的方法解决了计算量大的问题。在实验中，我们在上述定义的域内对变换后的数据进行了低频分量的减少，并证实了可以以较小的误差重建原始的光线空间数据。

In this research, we proposed a new compressive sensing method of Ray-Space in circular camera array. The compressive sensing utilizes special characteristics of Ray-Space that transformed data has sparse data structure. As the first step, we defined a new transform and inverse transform method between Ray-space and a new domain, where the amount of information can be efficiently reduced. We used an integral transform using the feature of Ray-space in polar coordinate system. We solved the problem of high computation cost by introducing successive approximation. In the experiment, we reduced low-frequency components of transformed data in the domain defined above, and confirmed that the original Ray-space data can be reconstructed with small error.