Heliograph Imaging Based on Compressed Sensing with Mean Shift Regularization
Heliograph Imaging Based on Compressed Sensing with Mean Shift Regularization
复制标题
基于均值平移正则化压缩感知的日光成像
DOI：
10.18494/sam.2014.997
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发表时间：
2014
影响因子：
1.2
通讯作者：
Zhou, Na
中科院分区：
文献类型：
--
作者：
Wang, Shuzhen;Wang, Ying;Xu, Liya;Wang, Yinlong;Guan, Shuyang;Zhou, Na
日像仪成像是从稀疏的频域数据中重建太阳图像的过程，压缩传感(CS)算法已经显示出从高度欠采样的数据中精确恢复图像的潜力。然而，CS对噪声很敏感，并且经常受到不希望看到的卷积伪影的影响。本文提出了一种改进的均值漂移正则化CS算法，该算法有助于抑制卷积伪影，重建太阳精细结构。已经使用合成图像和真实图像进行了一系列实验。结果表明，与其他方法相比，我们提出的算法具有更小的重建误差。
Heliograph imaging is the process of reconstructing a solar image from sparse frequency domain data, and the compressed sensing (CS) algorithm has shown potential to accurately recover images from highly undersampled data. However, CS is sensitive to noise and often suffers from undesired convolutive artifacts. In this paper, we present an improved CS algorithm with mean shift regularization, which helps to suppress the convolutive artifacts and reconstruct the solar fine structures. A set of experiments have been conducted using both synthetic and real images. The results demonstrate that our proposed algorithm has much smaller reconstruction errors than those of other methods.