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

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Lee, Dongwon; Steeb, Holger, 2023, "Image enhancement code: time-resolved tomograms of EICP application using 3D U-net", https://doi.org/10.18419/darus-2991, DaRUS, V1

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Description	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. https://doi.org/10.18419/darus-2227. 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 readme.txt file.
Subject	Computer and Information Science; Earth and Environmental Sciences; Engineering
Keyword	Enzyme Induced Calcite Precipitation (EICP), Porous Media, Porous Medium, Machine Learning, Micro X-Ray Computed Tomography (micro-XRCT)
Related Publication	Lee, D., Weinhardt, F., Hommel, J., Piotrovski, J., Class, H., and Steeb, H. (2022). Machine Learning assists in increasing the time resolution of X-Ray Computed Tomography applied to mineral precipitation on porous media. Scientific Reports, to be submitted.
License/Data Use Agreement	CC BY 4.0

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	data.7z 7Z Archive - 417.9 MB Published Feb 7, 2023 75 Downloads MD5: ab58b9cb6a76de1e09c5baa790eada71 Data for training and examples for pre/post-processing	Access File File Access Public Download Options 7Z Archive Download Metadata Data File Citation EndNote XML RIS BibTeX
	example.log Plain Text - 155 B Published Feb 7, 2023 23 Downloads MD5: 770701e71b665603a9e61292c7824135 An example of training log file (all the adopted parameters used during training are saved in this log)	Preview "example.log" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation EndNote XML RIS BibTeX
	Frac_Unet_postprocess.py Python Source Code - 5.7 KB Published Feb 7, 2023 17 Downloads MD5: ee7d79e5e2f70aabaae7bae129627301 Post-processing code to create enhanced images	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation EndNote XML RIS BibTeX
	Frac_Unet_preprocess.py Python Source Code - 2.4 KB Published Feb 7, 2023 21 Downloads MD5: 9b63a89131ad52136b92531a6b7f5e71 Pre-processing code to change the images into available shape and format	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation EndNote XML RIS BibTeX
	Frac_Unet_training.py Python Source Code - 7.0 KB Published Feb 7, 2023 15 Downloads MD5: 10e1540fe5afda49692b2437eb245903 Training code in order to train the model	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation EndNote XML RIS BibTeX
	img_seg.py Python Source Code - 17.5 KB Published Feb 7, 2023 23 Downloads MD5: 3a13bef31e567313fc8ef766bc00d377 Customized image library in order to save and load the image data	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation EndNote XML RIS BibTeX
	readme.txt Plain Text - 2.1 KB Published Feb 7, 2023 24 Downloads MD5: d378b2f2e6e39b343cb17bd2522c6b7d Detailed description of operation with codes	Preview "readme.txt" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation EndNote XML RIS BibTeX
	unet3D_v1.py Python Source Code - 3.8 KB Published Feb 7, 2023 19 Downloads MD5: c84b9fd76ff97e75a91ccde5cc1395e2 3D U-net model	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation EndNote XML RIS BibTeX

Citation Metadata

Persistent Identifier	doi:10.18419/darus-2991
Publication Date	2023-02-07
Title	Image enhancement code: time-resolved tomograms of EICP application using 3D U-net
Author	Lee, Dongwon (Institute of Applied Mechanics (CE), University of Stuttgart) - ORCID: 0000-0002-5359-7803 Steeb, Holger (Institute of Applied Mechanics (CE) & SC SimTech, University of Stuttgart) - ORCID: 0000-0001-7602-4920
Point of Contact	Use email button above to contact. Lee, Dongwon (University of Stuttgart) Steeb, Holger (Institute of Applied Mechanics (CE) & SC SimTech, University of Stuttgart)
Description	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. https://doi.org/10.18419/darus-2227. 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 readme.txt file.
Subject	Computer and Information Science; Earth and Environmental Sciences; Engineering
Keyword	Enzyme Induced Calcite Precipitation (EICP) https://encyclopedia.pub/entry/8160 Porous Media https://www.wikidata.org/wiki/Q3271208 (Wikidata) Porous Medium https://www.wikidata.org/wiki/Q3271208 (Wikidata) Machine Learning https://www.wikidata.org/wiki/Q2539 (Wikidata) Micro X-Ray Computed Tomography (micro-XRCT) https://www.wikidata.org/wiki/Q3042540 (Wikidata)
Topic Classification	Software (LCSH) http://id.loc.gov/authorities/subjects/sh99001417
Related Publication	Lee, D., Weinhardt, F., Hommel, J., Piotrovski, J., Class, H., and Steeb, H. (2022). Machine Learning assists in increasing the time resolution of X-Ray Computed Tomography applied to mineral precipitation on porous media. Scientific Reports, to be submitted.
Language	English
Producer	Lee, Dongwon (Institute of Applied Mechanics (CE), University of Stuttgart)
Production Date	2022
Production Location	University of Stuttgart, Institute of Applied Mechanics (CE), Stuttgart, 70569, Germany
Funding Information	DFG: 327154368 - SFB 1313
Project	SFB 1313 (Level 1) Project B05 (Level 2) Project C04 (Level 2) Project C05 (Level 2)
Distributor	(University of Stuttgart (Institute of applied mechanics (CE)))
Distribution Date	2022-06-27
Depositor	Lee, Dongwon
Deposit Date	2022-06-15
Data Type	program source code
Software	X-ray image enhancement, Version: v.1
Related Dataset	Lee, D., Weinhardt, F., Hommel, J., Class, H., and Steeb, H. (2022). Time resolved-micro-XRCT dataset of Enzymatically Induced Calcite Precipitation (EICP) in sintered glass bead columns". DaRUS. https://doi.org/10.18419/darus-2227
Did it work?	Yes
Explanation	By applying the code, low quality X-ray images were able to be enhanced.

Process Metadata

Software	Python Version: 3.7.7 (https://www.python.org/) Open

Software Metadata (CodeMeta 2.0)

Software Version	1
Development Status	Active
Application Category	Visualization
Application Subcategory	Image enhancement
Programming Language	Python; MATLAB
Operating Systems	Windows 10
Software Requirements	Python 3.7.7 https://www.python.org/
Software Suggestions	keras 2.3.1 https://github.com/keras-team/keras pandas 1.0.3 https://pandas.pydata.org Tensorflow 2.1.0 https://www.tensorflow.org/ scipy 1.4.1 https://www.scipy.org scikit-image 0.16.2 https://scikit-image.org numpy 1.18.1 https://www.numpy.org patchify 0.2.3 https://github.com/dovahcrow/patchify.py
Memory Requirements	64GB of Ram, 4GB of GPU memory
Storage Requirements	Free space required

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