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31 to 40 of 63 Results
Gzip Archive - 8.6 GB - MD5: 9bb6b4bf63d921455a11f3422fc85b91
Reconstructed micro-XRCT data sets (top and bottom part) in 16 bit *.tif file format. 1250x1250x2305 voxels each with a uniform voxel size of 14.0 µm. (internal ID: 20220216_01)
Gzip Archive - 13.3 MB - MD5: 61db5ecdd97e62ab0956fa9f012c9f95
Binarized micro-XRCT data set in 8 bit *.tif file format. 625x625x1655 voxels with a uniform voxel size of 28.0 µm (2x2x2 binned).
Gzip Archive - 33.5 GB - MD5: 754087605515ad8df4e150645f227bd1
Projection images (360 deg, angle increment 0.2 deg, top and bottom part), dark image (di) and open beam image (ob) in 16 bit *.tif file format. (internal ID: 20220216_03)
Gzip Archive - 8.9 GB - MD5: 7e3231d52342b9e78cd48725359ea8a3
Reconstructed micro-XRCT data sets (top and bottom part) in 16 bit *.tif file format. 1250x1250x2305 voxels each with a uniform voxel size of 14.0 µm. (internal ID: 20220216_03)
7Z Archive - 417.9 MB - MD5: ab58b9cb6a76de1e09c5baa790eada71
Data for training and examples for pre/post-processing
Plain Text - 155 B - MD5: 770701e71b665603a9e61292c7824135
An example of training log file (all the adopted parameters used during training are saved in this log)
Python Source Code - 5.7 KB - MD5: ee7d79e5e2f70aabaae7bae129627301
Post-processing code to create enhanced images
Python Source Code - 2.4 KB - MD5: 9b63a89131ad52136b92531a6b7f5e71
Pre-processing code to change the images into available shape and format
Python Source Code - 7.0 KB - MD5: 10e1540fe5afda49692b2437eb245903
Training code in order to train the model
Python Source Code - 17.5 KB - MD5: 3a13bef31e567313fc8ef766bc00d377
Customized image library in order to save and load the image data
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