抚仙湖太阳观测和研究基地知识库

针对云南天文台拍摄的模糊太阳斑点图像使用有监督学习模型进行重建时容易产生伪像、训练时间长、重建结果过分依赖参考图像等问题，提出一种基于结构重参数化与多分支模块相结合的弱监督去模糊方法重建太阳斑点图。首先，结合单尺度与多尺度网络设计去模糊模型，在模型中构造多分支模块提取不同尺度的特征，增强细节信息，减少伪像生成；其次，对每个分支结构进行重参数化，使得结构参数的重用贯穿整个特征提取过程，节省计算时间；之后，将去模糊模型分别嵌入退化学习与逆退化学习的弱监督训练中，先对模糊图像进行等级划分，利用退化模型分别学习不同等级的退化，构成对应等级的配对数据集，再使用去模糊模型对数据集进行逆退化，实现太阳斑点图的重建。实验结果表明，该方法与现有深度学习去模糊方法相比，模型训练效率更高，对参考图像的依赖较小，能够满足太阳斑点图像高分辨率重建要求。 

With the supervised deep learning algorithms, it is prone to generate artifacts when restoring the blurred solar speckle images taken by Yunnan Observatories, and it has a long training time and over-reliance on reference images, this paper proposed a weakly supervised method based on structural reparameterization combined with multi-branch module to reconstruct solar speckle images. First, deblurring model combined single-scale and multi-scale network to design, with constructing multi-branch modules to extract features of different scales, enhance detailed information, and reduce the generation of artifacts; second, each branch structure re-parameterized to make the reuse of structure parameters runs through the entire feature extraction process; after that, the deblurring model embedded in the weakly supervised training, the blurred image assorted firstly, then the degradation model used to learn different levels of degradation. Constituted paired dataset of corresponding levels, and the deblurring model used to inversely degenerate the dataset to reconstruct solar speckle images. Experimental results show that compared with the existing deblurring method, the proposed method has higher model training efficiency and less dependence on reference images, which can meet the high-resolution reconstruction requirements of solar speckle images.