This course will provide an introduction to some basic mathematical problems in image formation and image reconstruction. In addition to modelling forward problems, we consider classical regularization strategies, ensuring well-posedness of the image reconstruction problems. Beyond this classical setting, we dive into modern deep-learning methods, which allow solving inverse problems in a data-dependent approach. Finally, we also consider uncertainty quantification, where we employ the Bayesian view point of inverse problems.