Random cropping with scaling option for super-resolution data augmentation

Is your feature request related to a problem? Please describe.
In super-resolution networks, a low resolution input is upsampled using a neural networks often with an integer factor (e.g. x2, x3, x4 etc). When performing data augmentation, a good approach is to use random cropping with fixed size, especially if input images are bigger than what can fit in memory for network activations.

Describe the solution you'd like
To address the data augmentation issue of random cropping, a possible solution could be to get a random crop window in low resolution image and apply the same (but scaled up) window to target ground truth image.

For example, in a x4 upsampling network cropping a 56 x 56 window in low res input will correspond to 224 x 224 window cropping in target ground truth

Describe alternatives you've considered
Writing my own MONAI transforms, or manually cropping data

Additional context
This may be useful for super-resolution, upsampling or demosaicing networks that typically take low res input and up resolve it

Apologies if this is addressed by an existing transform, in which case can someone guide me on how I can achieve the above with an existing transform...

Review MONAI's existing transform APIs and any guidance for super-resolution or paired image augmentation; the issue does not name a file or test to start from. Define the crop relationship between the low-resolution input and scaled ground-truth target, then verify that the transform preserves the requested scale and crop sizes.