Disclaimer: Noise2Void improves the visual quality of an image. However, it is uncertain whether or not signals remain scientifically accurate. Use at your own risk.
Useful for:
- Visualization
- Intermediary step for mask generation
- Segmentation
Credits:
1. Start Noise2Void with the icon on the desktop: N2V_OMERO
2. Open the Login dialog and insert your credentials for the server omero-cai.hhu.de
![](/download/attachments/394790087/_GUI_N2V_login.png?version=1&modificationDate=1654000490442&api=v2)
3. Get ready for the training of a model for your images:
- Select a dataset on OMERO with its ID
- Get an image to test the connexion
- → Jump to 5. if you already have a trained model
- Choose a name for the model
- Select the path where to save the model
- "Number of training epochs" and "Batch size" can be left as default (if there is a memory error, for the training, the batch size must be reduced).
- Click on "Start training" and go for a break. This should last 10-15 min.
![](/download/attachments/394790087/_GUI_N2V_train.png?version=1&modificationDate=1654000959874&api=v2)
4. Look at the result with "Preview Result" and use the navigation pane to observe the before/after
![](/download/attachments/394790087/GUI_N2V_done.png?version=1&modificationDate=1654001486708&api=v2)
5. Apply the model to the dataset and upload results to OMERO → Start with this step if you already have a trained model for your images