Gaussian process regression (GPR) is an effective statistical learning method for modeling non-linear mapping from an observed space to an expected latent space. When applying it to example learning-based super-resolution (SR), two outstanding issues remain. One is its high computational complexity restricts SR application when a large dataset is available for learning task. The other is that the commonly used Gaussian likelihood in GPR is incompatible with the true observation model for SR reconstruction. To alleviate the above two issues, we propose a GPR-based SR method by using dictionary-based sampling and student-t likelihood, called DSGPR. Considering that dictionary atoms effectively span the original training sample space, we adopt a dictionary-based sampling strategy by combining all the neighborhood samples of each atom into a compact representative training subset so as to reduce the computational complexity. Based on statistical tests, we statistically validate that Studentt likelihood is more suitable to build the observation model for SR problem. Extensive experimental results show that the proposed method outperforms other competitors and produces more pleasing details in texture regions.