Recently, deep neural networks have demonstrated remarkable efficiency at improving the resolution of low-resolution (LR) images. This is called the image super-resolution (SR) task. Numerous researchers have come up with network architectures to deal with the problem of multi-image super-resolution (MISR) by using supervised learning. This entails the availability of ground-truth high-resolution (HR) pictures for the purpose of training. However, collecting LR and HR images from the same device to avoid introducing any additional mismatch between images could be problematic, especially for satellite images, which involve cameras hundreds of kilometres from the subject. The goal of this thesis is to provide a method to deal with MISR in a blind, or unsupervised, setting by leveraging other well-known networks to make blur kernels from LR images and do super-resolution.