The aim of this thesis is to develop a Machine Learning algorithm for Multi-image Super-resolution (MISR). Super-resolution is a well known image processing problem, whose aim is to process low resolution (LR) images in order to obtain their high resolution (HR) version. The super-resolution process tries to infer the possible values of missing pixels in order to generate high frequency information as coherently as possible with the original images. In general, we can distinguish between two different super-resolution approaches: Single-image Super-resolution (SISR) and Multi-image Super Resolution (MISR). The former tries to build the best LR-HR mapping analysing the features of a single LR image, while the latter takes as input multiple LR images exploiting the information derived from the small differences between the images such as changes in the point of view position and orientation, in the lightening condition and in the image exposition and contrast.