This chapter mainly discusses the image super-resolution technology based on deep learning. Firstly, the basic concepts of deep learning and neural networks, as well as common network structures, are introduced to help readers understand the core knowledge in this field. Next, the main datasets and image quality evaluation metrics commonly used for super-resolution image reconstruction are summarized to lay the foundation for algorithm evaluation. Subsequently, this chapter provides a detailed analysis of various super-resolution image reconstruction algorithms based on deep learning, focusing on their network structures, learning mechanisms, and applicable scenarios, as well as their respective advantages and limitations.