Image super-resolution is a well-known problem in computer vision that aims to enhance a low-resolution image into a high-resolution one. In recent years, deep neural networks have achieved remarkable results in tackling image super-resolution tasks, often employing large models and subsequently huge amounts of computational resources. However, the wide diffusion of mobile devices and portable photography necessitates the design of computationally efficient solutions. This thesis aims to address these challenges by adapting an existing lightweight neural network designed for single-image super-resolution to perform burst super-resolution while preserving its mobile-friendly characteristics. The main goal is to improve the quality of super-resolved images, assessed using the Peak Signal-to-Noise Ratio (PSNR) metric, by inputting more than one image into the network, while trying to keep the model’s inference time as low as possible.