Abstract: Inverse science comprises decoding experimental observations of a given object to decipher various properties encoded within the object. In this talk, two of the most popular classes of inverse problem, computed tomography and super-resolution, will be presented. In the case of computed tomography, the inverse problem is formulated as the maximum a posteriori probability (MAP) estimation problem, and a MAP cost function is iteratively minimized to deduce a most likely reconstructed result. Likewise, in the case of super-resolution, the convolutional neural networks tool is used to scale up low-resolution images to their high-resolution counterparts. The final assessment of the results obtained from our approaches on both cases is made by means of quantitative metrics such as root-mean-square error, peak signal-to-noise ratio, and structural similarity indxe for synthetic as well as experimental datasets.