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In this paper, a super-resolution (SR) method through an internal and external dictionary-based regularization was proposed for improving the resolution of single low-resolution (LR) image. Firstly, the input was divided into several similar groups, and then the principal components analysis (PCA) dictionary learning method was used to construct the internal dictionary of each corresponding group. Secondly, the high-frequency details of external high-resolution (HR) images was divided into groups with similar structures, and the PCA method was adopted to construct the external dictionary corresponding to each group. Next, the nonlocal regression model was used to design two complementary regularities to solve the problem of SR uncertainty. Finally, the SR reconstruction was achieved through an iterative optimization algorithm with gradient descent. The experimental results showed that the proposed method could improve the peak signal to noise ratio (PSNR) by 0.2 dB on average and the structural similarity (SSIM) by 0.01 on average, showing better visual quality than the other compared methods.