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Simulation of flow directly at the pore scale depends on high-quality digital rock images but is constrained by detector hardware. A trade-off between the image field of view (FOV) and image resolution is made. This can be compensated for with superresolution (SR) techniques that take a wide FOV, low-resolution (LR) image, and superresolve a high resolution (HR). The Enhanced Deep Super Resolution Generative Adversarial Network (EDSRGAN) is trained on the DeepRock-SR, a diverse compilation of raw and processed micro-computed tomography (mu CT) images in 2D and 3D. The 2D and 3D trained networks show comparable performance of 50% to 70% reduction in relative error over bicubic interpolation with minimal computational cost during usage. Texture regeneration with EDSRGAN shows superior visual similarity versus Super Resolution Convolutional Neural Network (SRCNN) and other methods. Difference maps show SRCNN recovers large-scale edge features while EDSRGAN regenerates perceptually indistinguishable high-frequency texture. Physical accuracy is measured by permeability and phase topology on consistently segmented images, showing EDSRGAN results achieving the closest match. Performance is generalized with augmentation, showing high adaptability to noise and blur. HR images are fed into the network, generating HR-SR images to extrapolate network performance to subresolution features present in the HR images themselves. Underresolution features are regenerated despite operating outside of trained specifications. Comparison with scanning electron microscopy (SEM) images shows details are consistent with the underlying geometry. Images that are normally constrained by the mineralogy of the rock, by fast transient imaging, or by the energy of the source can be superresolved accurately for further analysis.

Plain Language Summary When capturing an X-ray image of the insides of a rock sample (or any opaque object), hardware limitations on the image quality and size exist. These limitations can be overcome with the use of machine learning algorithms that "superresolve" a lower resolution image. Once trained, the machine algorithm can sharpen otherwise blurry features and regenerate the underlying texture of the imaged object. We train such an algorithm on a large and wide array of digital rock images and test its flexibility on some images that it had never seen before, as well as on some very high quality images that it was not trained to superresolve. The results of training and testing the algorithm shows a promising degree of accuracy and flexibility in handling a wide array of images of different quality and allows for higher quality images to be generated for use in other image-based analysis techniques.