Interferometric Synthetic Aperture Radar (InSAR) is a measuring technology that uses the phase information contained in the images of the Synthetic Aperture Radar (SAR). InSAR has been recognized as a potential method for digital elevation models (DEMs) generation and ground surface deformation measurement. Nonetheless, the quality of InSAR data is influenced by many critical factors. Amongst these factors are image co-registration, interferogram generation, phase unwrapping and geocoding. Image co-registration aims to align two or more images in such a way that the same pixel for each image corresponds to the same point of the target scene. Interferogram generation is used for mapping the deformation of the ground using two SAR images capturing the same area from slightly different look angles. This study proposes a new algorithm for improving Image co-registration and interferogram generation of SAR using learning-based images super-resolution. In this study, we show that our approach improves the conventional approaches used for image co-registration and interferogram generation achieving images of higher coherence.