Dear fellow users:

I am trying to denoise 2d MR images. The paper I am trying to start off from is based on “Beyond a Gaussian Denoiser”. So, I have a train folder (im + noise) and a target/ground truth folder ( im ). The network in itself is simple in that it has a bunch of conv, BN, Relu operation without any pooling. So, the size of output from the network is the same as that of the input. They then find the MSE loses with ground truth and train the model.

How do I generate the Image Databunch. I was hoping to use something along the lines of

```
data = (src.label_from_func(lambda x: path_hr/x.relative_to(path_lr))
.transform(get_transforms(max_zoom=2.), size=size, tfm_y=True)
.databunch(bs=bs).normalize(imagenet_stats, do_y=True))
data.c = 3
return data
```

data.c looks like the number of classes and I am not working on a classification problem. So, I would appreciate if you could give me some information as to how to proceed with it.

Best regards,

Vishwa