Comments (1)
Noise2Void has to be trained on pixel-wise independent noise. Crumpling or creases span multiple pixels, which makes them not pixel-wise independent. Since N2V has only one picture to train, it is impossible for the network to distinguish between real structure and noise patterns. We discuss this limitation in our paper (https://arxiv.org/pdf/1811.10980.pdf) in Section 4.4 Errors and Limitations.
This being said, the network architecture is able to learn the removal of non-pixel-wise independent noise. But you would need a pairs of training images where the input image has crumpling or creases and the target images has no such patterns. Figure 6 in the paper illustrates this well.
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Related Issues (20)
- Problem with config HOT 5
- denoising 1D data HOT 2
- Issue with creating the network model from StructN2V HOT 2
- Unable to get sample notebooks to run HOT 5
- GPU training doesn't work? HOT 5
- Fiji error when runnnig n2v-sem demo model with bioimage.io HOT 3
- Tensorflow-gpu version 2.4.1 is not compatile with Windows HOT 12
- 'ReduceLROnPlateau' object has no attribute '_implements_predict_batch_hooks' HOT 2
- making patch size smaller in the given 3D example notebook lead to nan loss HOT 2
- Examples fails due to auth login data HOT 4
- Prediction: allocation exceeds 10% of system memory
- kernel dies during prediction after "allocation of system memory exceeds 10%"
- 2D RGB data not accessible anymore: 502 Bad Gateway HOT 5
- Issue with XLA devices HOT 1
- Issue when training HOT 1
- The direction of the 3D data test result is inconsistent with the original data HOT 4
- Check license / copyright holders HOT 1
- Verbosity management
- Model export does not work for N2V2 HOT 1
- Add 3D support for N2V2
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