Closed tsuijenk closed 2 years ago
Because our SUNet is trained by RGB images, we don't provide the pre-trained model for grayscale denoising.
However, if you want to retrain a grayscale model, you can simply change from out_chans=3
to out_chans=1
in model/SUNet.py.
https://github.com/FanChiMao/SUNet/blob/30eeb03395a9e275a5700914dfec0b71d12eb613/model/SUNet.py#L12
After modifying this, and if you already have the grayscale pair training data, you can directly run the training code (train.py) without adjusting the model architecture.
Notably, in model/SUNet.py, if the dimension size (x.size()[1]
) is 1 (grayscale images) it will "repeat" them to 3 dimensions as shown below.
So, we don't need to modify the in_chans
.
Hope this can help you!
How can we train the model in case of hyperspectral datasets like these http://lesun.weebly.com/hyperspectral-data-set.html ? They have >3 channels
Hello.
Because our SUNet is built for image denoising task which the channel of input and output images is usually set to 3,
we don't add the parameters in training.yaml.
However, you can directly modify the in_chans
and corresponding output channels out_chans
for image segmentation in here.
Hello,
Great work! I was just wondering if there's any way we can modify the model so that it works for gray-scale image.
Thank you.