Super-resolution imaging is a technique that enhances spatial resolution from low-resolution image frames. Based on the number of low-resolution images input into the imaging system, super-resolution can be classified into single-frame and multi-frame super-resolution reconstruction. This chapter delves into the fundamental principles and methodologies of single frame super-resolution, with a focus on key algorithms and approaches such as interpolation-based and reconstruction-based methods, learning-based models, and edge-preserving techniques. We discuss the challenges faced in this field, including noise amplification and computational complexity, and explore recent advancements that have significantly improved performance. The chapter also provides an overview of the important applications of single-frame super-resolution in fields such as medical imaging, satellite imagery, and consumer electronics, emphasizing its significance in high-resolution image reconstruction.