针对天文图像成像分辨率低的问题,基于集中稀疏表示图像超分辨率重建理论,提出一种层次聚类字典训练和相似约束的天文图像超分辨率重建算法。在字典训练阶段,采用新的基于层次的聚类算法对样本图像块进行归类,对每类图像块进行独立训练得到多个紧凑型字典。在图像重建阶段,通过抑制稀疏编码噪声提高稀疏编码系数的准确性,并利用图像的非局部自相似性对重建图像的稀疏系数进行合理估计。此外,通过构建非局部自相似正则化项对图像重建过程进行全局约束。仿真结果表明,该算法可以有效地改善天文图像的分辨率,重建图像在主观视觉效果和客观评价指标上都要优于其他传统的超分辨率重建算法。

This study proposes a super-resolution reconstruction algorithm with hierarchical clustering dictionary training and similar constraints for astronomical images, according to the theory of centralized sparse representation based image super-resolution reconstruction, thereby solving the problem of low imaging resolution of the astronomical images. In the dictionary training phase, a novel hierarchical clustering algorithm is used for classifying the sample image patches. Further, each image patch is independently trained to obtain multiple compact dictionaries. In the image reconstruction stage, the accuracy of the sparse coding coefficients is improved by suppressing the sparse coding noise. Subsequently, the sparse coefficients of the reconstructed image can be reasonably estimated based on the non-local self-similarity of the image. In addition, the image reconstruction process is globally constrained by the construction of non-local self-similar regularization terms. The experimental results denote that the proposed algorithm can effectively improve the resolution of astronomical images. Furthermore, the subjective visual effects and objective evaluation indicators of the reconstructed images are observed to be superior to those obtained by using other traditional super-resolution reconstruction algorithms.