Micro-computed tomography (micro-CT) is an imaging technique to examine the material's inner microstructure in detail (down to microscale) and even predict mechanical properties. The main limitation of micro-CT is a small field of view for high-resolution scans: only small and often non-representative specimens can be scanned to obtain detailed microstructure. In addition, high-resolution images take more time and are not resistant to x-ray artifacts. One solution is to synthetically increase the spatial resolution of scans in post-processing and remove defects by inpainting. Increasing resolution (super-resolution) techniques are widely used in different fields and have evolved rapidly by using deep learning algorithms. This work investigates deep learning-based methodologies and algorithms for super-resolution for micro-CT images of composite materials, its application for enabling automated fiber breaks detection for low resolution images.