deweihu / oct_ddpm Goto Github PK
View Code? Open in Web Editor NEWApplication of Diffusion Probablistic Model for unsupervised OCT denoising
License: MIT License
Application of Diffusion Probablistic Model for unsupervised OCT denoising
License: MIT License
Your readme should say what paper users should cite!
Maybe this one?
https://doi.org/10.1117/12.2612235
Thank you for your excellent work and sharing the code.
Is the dataset in the paper public or private?
If it is public, can you provide the link to the dataset?
Hello! Great job!
I would like to ask you how to use three different levels of signal-to-noise ratio (92dB, 96dB, 101dB) to obtain pictures?
As you said in paper: To test the performance of the model at different speckle levels, the data is acquired with three different levels of signal-to-noise ratio (92dB, 96dB, 101dB).
I want to use my own dataset, but I don't know how to do this step, can you give some advice? Thank you very much!
Is there any requirement for the grayscale range of data?
Do I need to normalize the data to [0,1] ?
Thank you very much!
First of all, thank you very much for your work, I hope you can open source the complete code, including training, testing, etc. If it is not convenient to open source, Could you send it to me privately, thank you very much.
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