抚仙湖太阳观测和研究基地知识库

抚仙湖一米真空太阳望远镜Level 1数据是利用图像选帧位移叠加技术获得的,目前Level 1处理的时间约20 s,大约是图像采集时间的1.7倍.为了达到减少其所需时间的目的,本文采用GPU技术来实现选帧流程,包括斑点干涉选帧和互相关图像位移叠加.在CUDA环境下,实现了利用GPU处理大量傅里叶变换和图像矩阵的并行四则运算等功能.在我们的实验环境下,对100帧图像的处理只需要0.6 s.同时对整个流程中各个模块的运行时间也进行了详细的测量、分析和讨论.

The sun high resolution imaging observation is the most important way of New Vacuum Solar Telescope (NVST) located in Fuxian Lake of Yunnan. This paper mainly describes the method of the Level 1 data processing of NVST. Currently, the times used by NVST to calculate the 100 images is about 20 s, which is almost 1.7 times of 100 image acquisition time of 12 s. It can’t meet the demand of real time processing. In this situation, we have decided to use GPU to process the data. Because the GPU has multi-threading parallelism and a superiority in image processing. We implemented the algorithm by GPU to short the processing time to meet the demand without changing the original algorithm. Thus, it ensures the accuracy of the original operation without affecting the subsequent operation. The experiment includes two main parts, frames-selection method and shift-and-add method. Frames-selection uses speckle interferometry (SI). It selects part of the images with better quality and then shifts-and-adds them. Not all of the image data are required. Usually we only concerned with the necessary part. So in the election process, we only calculated the frequency and the bandwidth. It will highly improve the accuracy of the selected frame. The Fourier transform spent the most time in this experiment. It runs three times in the frame selection and processes second times in the shift-and add. This part of the length of time consuming operation largely determines the efficiency of the program. The CUDA provides the standard library functions, which called CUBLAS and greatly improve the operating efficiency of the program. The library functions CUBLAS provides matrix operations. In this study, most of the matrix operations referenced standard library functions to achieve a large number of image matrix of the parallel arithmetic and other functions. The rest of them used a GPU multi-threading technology. In our experiments, 100 images calibration only takes 0.25 s, GPU runtime also only 0.6 s, the processing time of a single frame is only 4.7 ms. The time of processing image exposure spends only 12 ms. Therefore, it means that in the next frame of the image exposure time, the processing steps of the previous frame image exposure can be completed synchronously. In the current conditions, it meet the demand of real-time processing.