Single-Image super-resolution - When model adaptation matters

•In this paper, we propose a variation of deep residual convolutional neural networks with robustness and efficiency in both learning and testing.•More importantly, we propose multiple strategies for model adaptation to the internal contents of the lowresolution input image and analyze their strong points and weaknesses.•Our adaptation especially favors images with repetitive structures or high resolutions.•We hope to arouse the interests of communities in focusing on internal priors, which are limited but have been proved effective and highly relevant.