I am using CARE for a little while now for 3D image restoration and I love how user friendly and efficient it is!! However, I still have a couple of questions about the training part that I hope you have the answers for:
- I have multiple data sets containing different images with multiple laser powers (for example I can have the same microscope image with 5%/20%/50% and 100% laser power). Assuming from 50% and up we consider it Ground Truth quality level, does CARE do well when using images with different qualities in the training? For example pairing :
- low:5% -> GT:50%
- low:5% -> GT:100%
- low:20% -> GT:50%
- low:20% -> GT:100%
I would argue that it could increase the robustness of the network but I don’t have a deep enough understanding of machine learning to confirm or refute that.
- I played a bit with the training parameters and wanted to see if different ones gave better restoration. But I have a little trouble understanding what unet_n_depth and unet_n_first represent exactly when considering the UNET, especially when those were the ones that changed the performance the most significantly. I am guessing that unet_n_depth would be the depth of the network but didn’t find a way to confirm that.
I hope my questions were clear enough and thank you for taking the time to read them.
In any case, thank you for this great tool and for all the hard work that was put into making it.