| Table of Contents |
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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
| 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 | |
|---|---|
| Data Type | RGB Images |
| Data Size | |
| Data Source |
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.
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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:
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You need Jupyter notebook as well as all the Python packages necessary to run it.
Annotated Description
The repository is arranged as follows:
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- 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;
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/
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