Super-resolution (SR) image restoration is the process of producing a high-resolution image (or a sequence of high-resolution images) from a set of low-resolution images , , . The process requires an image acquisition model that relates a high-resolution image to multiple low-resolution images and involves solving the resulting inverse problem. The acquisition model includes aliasing, blurring, and noise as the main sources of information
loss. A super-resolution algorithm increases the spatial detail in an image, and equivalently recovers the high-frequency information that is lost during the imaging process.