  Deep neural networks have received considerable attention in clinical
imaging, particularly with respect to the reduction of radiation risk. Lowering
the radiation dose by reducing the photon flux inevitably results in the
degradation of the scanned image quality. Thus, researchers have sought to
exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose
images to higher-dose, higher-quality images thereby minimizing the associated
radiation hazard. Conversely, computed tomography (CT) measurements of
geomaterials are not limited by the radiation dose. In contrast to the human
body, however, geomaterials may be comprised of high-density constituents
causing increased attenuation of the X-Rays. Consequently, higher dosage images
are required to obtain an acceptable scan quality. The problem of prolonged
acquisition times is particularly severe for micro-CT based scanning
technologies. Depending on the sample size and exposure time settings, a single
scan may require several hours to complete. This is of particular concern if
phenomena with an exponential temperature dependency are to be elucidated. A
process may happen too fast to be adequately captured by CT scanning. To
address the aforementioned issues, we apply DCNNs to improve the quality of
rock CT images and reduce exposure times by more than 60\%, simultaneously. We
highlight current results based on micro-CT derived datasets and apply transfer
learning to improve DCNN results without increasing training time. The approach
is applicable to any computed tomography technology. Furthermore, we contrast
the performance of the DCNN trained by minimizing different loss functions such
as mean squared error and structural similarity index.
