Image enhancement code: time-resolved tomograms of EICP application using 3D U-net

This dataset contains the codes to reproduce the results of "Time resolved micro-XRCT dataset of Enzymatically Induced Calcite Precipitation (EICP) in sintered glass bead columns", cf. <a href="https://doi.org/10.18419/darus-2227">https://doi.org/10.18419/darus-2227</a>. The code takes "low-dose" images as an input where the images contain many artifacts and noise as a trade-off of a fast data acquisition (6 min / dataset while 3 hours / dataset ("high-dose") in normal configuration). These low quality images are able to be improved with the help of a pre-trained model. The pre-trained model provided in here is trained with pairs of "high-dose" and "low-dose" data of above mentioned EICP application. The examples of used training, input and output data can be also found in this dataset. Although we showed only limited examples in here, we would like to emphasize that the used workflow and codes can be further extended to general image enhancement applications. The code requires a Python version above 3.7.7 with packages such as tensorflow, kears, pandas, scipy, scikit, numpy and patchify libraries. For further details of operation, please refer to the <a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=134113">readme.txt file</a>.