为了抑制超分辨图像重建过程中的振铃锯齿效应，本文提出一种多正则化形式的超分辨率重建算法。文章首先给出了图像降质模型并推导出了图像重构约束项。利用重构项直接对低分辨率图像进行重建，获得的高分辨图像会有锯齿和振铃效应。针对此问题，本文利用自回归模型和滤波器组先验来正则化重建过程。自回归模型用来恢复图像局部细节描述，与此同时本文利用自然图像块的聚类集来估计自适应自回归模型参数。滤波器组先验用来约束重建图像的边缘，使得获取的高分辨率的图像边缘更加锐利。最后通过实验定性与定量的分析，证实了本文算法优于其他具有竞争力的算法。

In order to suppress the ringing and jaggy artifacts during the super-resolution image reconstruction process, an image super-resolution algorithm with multiple regularized terms is proposed. Firstly, the image degradation model is given and the image reconstruction constraint item is analytically derived. The high-resolution image can be generated by using the reconstruction constraint item, which will have jaggy and ringing artifacts. In order to solve this problem, the autoregression model and filters prior are invented to regularize the reconstruction process. The autoregression model is used to restore the local image details and the adaptive parameters of the autoregression model can be generated through the natural cluster sets. Meanwhile, the filters prior are used to force the edges of high-resolution image to be sharp. Finally, the experimental results show that our algorithm outperforms other competing algorithms in terms of both quantity and quality.