Table of Contents |
---|
Author(s)
Name | Institution | Mail Address | Social Contacts |
---|---|---|---|
Alessandro Bombini | Cultural Heritage Network (CHNet), INFN, Firenze on behalf of European Science Cloud (EOSC) - Pillar | bombini@fi.infn.it | Skype: ; Linkedin: https://it.linkedin.com/in/alessandro-bombini-7929a2133; Twitter: ; Hangouts: |
Skype: ; Linkedin: ; Twitter: ; Hangouts: |
How to Obtain Support
bombini@fi.infn.it | |
Social | Skype: ; Linkedin: ; Twitter: ; Hangouts: |
Jira |
General Information
ML/DL Technologies | CNN U-Net |
---|---|
Science Fields | Applied Physics |
Difficulty | Entry level |
Language | Eng |
Type | fully annoted, runnable |
Software and Tools
Programming Language | Python |
---|---|
ML Toolset | Keras |
Additional libraries | Sci-kit image, PIL, OpenCV, matplotlib |
Suggested Environments |
Needed datasets
Data Creator | Toronto University |
---|---|
Data Type | RGB Images |
Data Size | 175 MB |
Data Source | https://www.cs.toronto.edu/~kriz/cifar.html |
Short Description of the Use Case
Inpainting is a conservation process where damaged, deteriorating, or missing parts of an artwork are filled in to present a complete image. This process can be applied to both physical and digital art mediums such as oil or acrylic paintings, chemical photographic prints, 3-dimensional sculptures, or digital images and video.
...
This is a dummy case of Digital Restoration, showing a glimpse of how NN can be applied to Physical Imaging of Cultural Heritages.
How to execute it
The example is available in the GitLab repository:
...
You need Jupyter notebook as well as all the Python packages necessary to run it.
Annotated Description
The repository is arranged as follows:
...
- Image_Inpaiting_Tutorial.ipynb: annotated notebook containing the construction and the training of the model
- open_model.ipynb: annoted notebook where the model trained over damaged 32x32 images is applied;
- open_model_192x192.ipynb: annoted notebook where the model trained over damaged 192x192 images is applied;
Other:
- Tutorial Image Inpainting.pdf: pdf version of the slides used while presenting this notebook;
- Tutorial Image Inpainting.pptx: powerpoint version of the slides used while presenting this notebook;
References
- Cifar dataset: https://www.cs.toronto.edu/~kriz/cifar.html
- Original UNet paper: https://arxiv.org/abs/1505.04597
- Original ADAM optimazer paper: https://arxiv.org/abs/1412.6980
Attachments
https://gitlab.com/alessandro.bombini.fi/image-inpainting-tutorial/
...