Over the past few decades, significant advancements have been made in imaging technology, leading to unprecedented levels of image resolution. Despite these advancements, the demand for further resolution enhancement persists in various applications such as video surveillance, medical imaging, and remote sensing. While hardware improvements can achieve higher resolution, they are often prohibitively expensive, particularly for large-scale equipment. This chapter primarily discusses various algorithms related to super-resolution imaging, focusing on multi-frame super-resolution reconstruction, such as Papoulis-Gerchberg algorithm and compressed sensing. These algorithmic advancements have significantly propelled the capabilities of super-resolution imaging, facilitating detailed observations at the molecular level and advancing life sciences research.