Image super-resolution reconstruction (SR) aims to obtain high-resolution images from one or more low-resolution images. Recently

SR has been developing and widely applied in different fields. This survey retrospects the history of SR technique and provides a comprehensive overview of representative SR methods

with an emphasis on recent deep learning-based approaches. We elaborate the details of various deep learning-based SR methods

including their strengths and weakness

in terms of the deep learning model

architecture

and message pass. Finally

we discuss the possible research directions on SR technique.