Comments (4)
Hi, thanks for your interest in our work! It is expected behavior that less noisy images should be matched with small state numbers (since they are more close to the clean reference). Do you encounter any problem when running Stage3? Can you observe visual improvements between Stage3 results and Stage1 results?
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Thanks for the reply! I do have tried to just use the few matched states for stage 3, but the generated images are not so good. Did you change the beta schedule? I noticed you use beta_0 = 5e-5, beta_T = 1e-2, which seems different from that of DDPM (I remember they are 1e-4 and 2e-2). Does it mean changing the starting and ending of the schedule can make stage 2&3 have larger matched states?
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In our original implementation, as you mentioned, we changed the beta scheduler to have large matched states (more generation steps). Yes, changing beta schedule in your case may help denoise less noisy images.
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Thanks so much! I also plan to change the beta schedule. Can you share any insights about how to modify the beta schedule based on your estimated noise level? Also, I am wondering generally how many steps you will match for your dataset (DWI images). Thanks!
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Related Issues (20)
- Experimenting on DCE-MRI simulated phantom with added noise HOT 1
- Need for Stage 1 Model in Stage 3 training HOT 1
- Clarifications on dimensions HOT 7
- Environment conflicts with GPU HOT 3
- Configurations for executing code with the PPMI dataset HOT 2
- Evaluation HOT 5
- Question regarding dataset HOT 2
- Question about the second stage HOT 1
- Question about training data valid_mask[10,160]
- sampling process HOT 5
- Question about data['condition'] HOT 2
- Question about matched state? HOT 1
- Question about the diffusion training process. HOT 1
- voxel size change in denoised image HOT 1
- Questions about the third stage HOT 1
- Questions about datasets HOT 4
- Provide benchmark dataset and trained model for unit-test HOT 1
- Training Dataset question HOT 1
- Reproducing the FA map
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