Comments (3)
I am very interested in DGUNet and want to follow it because of its strong performance and godd interpretability.
However, FGDM does not seem to be suitable for image denoising because the noise degradation process is not deterministic (not Ax
but + n
)?
from deep-generalized-unfolding-networks-for-image-restoration.
Thanks for your attention and valuable suggestion! We have updated the evaluation for DND. In real-world scenarios, image denoising is not only related to additive noise (i.e., A!=I).
from deep-generalized-unfolding-networks-for-image-restoration.
Thanks for your reply!
from deep-generalized-unfolding-networks-for-image-restoration.
Related Issues (12)
- The model you provided failed to run
- Questions about the "RB" Block in the U-net architecture
- Why does it perform well during validation, but poorly during testing
- 训练流程 HOT 1
- Why training should be divided into python train.py python train_plus.py These two steps? HOT 1
- in the task of deraining, does it only take three days for both DGUNet and DGUNet+ networks to train on two NVIDIA Tesla V100 GPUs? HOT 1
- compressive sensing数据集与论文中有差异 HOT 2
- About Loss HOT 6
- 训练资源问题
- Issue about trainable measurement matrix A in compressed sensing mode HOT 1
- The training set for training compressive sensing task HOT 2
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from deep-generalized-unfolding-networks-for-image-restoration.