The mining industry has been witnessing a reduction of extracted iron ore s quality and the advent of new environmental demands. This situation reinforces a search for iron ore products that meet the requirements of the steel industry, such as new iron ore agglomerates. X-ray microtomography (microCT) allows the characterization of a sample s three-dimensional structure, with micrometer resolution, in a non-destructive analysis. However, this technique presents several limitations. Better resolutions greatly increase analysis time and decrease the acquired sample’s volume. Super-Resolution (SR) models, based on Deep Learning, are a powerful tool to digitally enhance the resolution of tomographic images acquired at lower resolutions. This work proposes the development of a methodology to train three SR models, based on EDSR architecture, using tomographic images of direct reduction briquettes: A model for enhancing the resolution from 16 um to 6 um, another for enhancing from 6 um to 2 um, and the third for enhancing 4 um to 2 um. This proposal aims to mitigate the limitations of microCT, assisting the development and implementation of new Digital Image Processing methodologies for agglomerates. The methodology includes different proposals for SR s performance evaluation, such as PSNR comparison and pore segmentation. The results indicate that SR can improve the resolution of tomographic images and reduce common tomography noise.