Fractured reservoir rocks are among the most critical targets in petroleum engineering studies, as their complex networks of fractures and cracks play a decisive role in the porosity and permeability of these formations. CT-scan imaging is a key tool for analyzing the internal structure of such rocks; however, reduced image quality often makes accurate reconstruction challenging. In this study, two deep-learning architectures a Convolutional Autoencoder and a Vision Transformer were designed and compared with the objective of enhancing and reconstructing CT-scan images of fractured reservoir rocks. The primary task of the models was to reconstruct and denoise subsurface fractured images, and following reconstruction, a three-class segmentation process (matrix, open fracture, and filled fracture) was performed using the classical Otsu thresholding method to evaluate the improved performance of the reconstructed outputs. Quantitative and qualitative results demonstrated that the Vision Transformer outperformed the autoencoder due to its use of the attention mechanism. The Vision Transformer achieved a peak signal-to-noise ratio of 45 and a structural similarity index of 0.98 in image reconstruction, whereas the autoencoder achieved values of 39 and 0.93, respectively. In the Otsu-based three-class segmentation, the Vision Transformer again delivered superior performance in terms of precision, recall, and F1-score. For the matrix class, precision, recall, and F1-score reached 0.989, 0.990, and 0.989, compared to 0.980, 0.969, and 0.974 for the autoencoder. For the filled-fracture class, the results were 0.974, 0.953, and 0.963, and for the open-fracture class, 0.923, 0.920, and 0.921, while the autoencoder recorded 0.960, 0.962, 0.961 and 0.940, 0.934, 0.937 for the same classes. Overall, the findings indicate that the Vision Transformer is a more efficient and accurate option for CT-scan image reconstruction of fractured rocks, offering significant potential to improve numerical analyses of rock properties such as porosity and permeability in reservoir engineering studies.