Comments (2)
Yes, we can use k-sigma transform to use the same model for multiple sensors.
But the distribution of k, sigma, or the noise profile, are still "defined" by the training data, i.e. the model cannot generalize to untrained noise profiles. So we cannot claim that this method is sensor-independent.
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Thanks for your reply.
What do you mean by noise profile? I think the final noise model is only related to f(x*). Can I have a contact information from you, if it's convenient, so that I can consult you in more detail?
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Related Issues (20)
- K-Sigma Estimation HOT 5
- confused about the training input range HOT 2
- Error when running evaluation code HOT 2
- Regarding training data augmentation
- about training data
- k-Sigma transform make signal ISO-Independent???
- testing our own images
- Why there is relu at the end of each encoder block, but no relu at the end of decoder?
- Reproducing reno10x_noise
- cannot open torch_pretrained.ckp and json.load(f) error HOT 3
- confuse with raw data load
- is anyone can use train.py from https://github.com/bigeagle/PMRID/tree/main
- run_benchmark.py KSigma Transform HOT 3
- Baseline models and MACs calculator
- How to understand the meaning of anchor? How to choose this value? thank you HOT 4
- "doubt about K_sigma transform"
- what's the purpose of this code ? HOT 2
- The train script HOT 10
- python3 run_benchmark.py --benchmark /path/to/PMRID/benchmark.json models/mge_pretrained.ckp没反应
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