Kazemivala Romina, Nikolova Natalia K
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada.
Sensors (Basel). 2024 Jun 14;24(12):3849. doi: 10.3390/s24123849.
A novel image-reconstruction method is proposed for the processing of data acquired at random spatial positions. The images are reconstructed and updated in real time concurrently with the measurements to produce an evolving image, the quality of which is continuously improving and converging as the number of data points increases with the stream of additional measurements. It is shown that the images converge to those obtained with data acquired on a uniformly sampled surface, where the sampling density satisfies the Nyquist limit. The image reconstruction employs a new formulation of the method of scattered power mapping (SPM), which first maps the data into a three-dimensional (3D) preliminary image of the target on a uniform spatial grid, followed by fast Fourier space image deconvolution that provides the high-quality 3D image.
提出了一种用于处理在随机空间位置采集的数据的新型图像重建方法。图像在测量的同时实时重建和更新，以生成一个不断演变的图像，随着数据点数量随着额外测量流的增加而增加，其质量不断提高并收敛。结果表明，这些图像收敛于在均匀采样表面上采集的数据所获得的图像，其中采样密度满足奈奎斯特极限。图像重建采用了散射功率映射（SPM）方法的一种新公式，该方法首先将数据映射到均匀空间网格上目标的三维（3D）初步图像中，然后进行快速傅里叶空间图像去卷积，以提供高质量的3D图像。
