太阳观测已经全面进入到高空间和高时间分辨率的时代，观测资料也从单帧图像进入到序列图像。本项目针对这一发展，旨在专门研究太阳高分辨序列图像处理中的一些关键性问题，从而有效提高定量处理动态特征的能力。研究内容包括数据的预处理、不同运动特征的目标提取和分解、多尺度速度场的测量。本研究并不拘泥于处理某种特定波段的太阳特征结构，而是着力于研究具有普适性通用方法的关键技术。强调多帧序列图像之间的相关性，通过构造三维时空立方体，结合时间域、空间域、频率域以及多尺度、多分辨率信息，充分利用三维图像处理技术达到提取、跟踪、分解、测量太阳活动信息之目的。拟解决的关键问题包括：基于测量平差理论的序列图像的视场对齐；基于Kalman滤波的序列图像的滤波；基于三维傅里叶变换的速度滤波和基于多尺度分析的速度场的测量。研究成果不但将直接应用于我国NVST和美国NST数据的处理，而且还可以应用到其他太阳观测序列图像。

A new era of solar observation is coming. The spatial and temporal resolution of observed data is much higher than before, and style of data changes from a single image to image sequence. Our propose focuses on the key technique of processing high-resolution image sequence for effectively improving the ability of dealing with solar dynamic features in quality. The details include data preprocessing, moving features extraction and decomposition, multi-scale velocity field measurement etc. This study is not limited in any special feature structure in the sun, but focus on common approach. The multi-frame relationship in image sequence is emphasized. By constructing a three-dimensional space-time cube, the information in time domain, spatial domain, frequency domain .and multi-scale, multi-resolution are combined to extract, track, decompose, and measure features in the sun. The key issues to be addressed include image sequences alignment technique based on Surveying Adjustment Theory, Kalman filtering for image sequence, velocity filter designing based on three-dimensional Fourier transform and velocity field measurement based on multi-scale analysis. Research results will be directly applied in NVST and NST data processing, and also can be employed for other solar observation image sequence.