Comments (1)
The numbers are chosen by experimenting with varying sizes of patches. There is not a mathematical relation (if that's what you are asking). The intuition is that more challenging tasks would require more information to fill in the gap. I'm not sure what you meant by 'only rgb' since the patches are rgb patches?
I didn't have experiments with a 1X zoom, which I guess would be a model that learns the camera's ISP. I think it can be a rather easy task to learn, you can start out with the same setting e.g. n=10.
from zoom-learn-zoom.
Related Issues (20)
- the sky is dirty issue HOT 1
- The value of white_lv HOT 2
- How to use tform.txt and wb.txt during training? HOT 1
- Have you aligned the train dataset ? HOT 1
- about train code
- Dear author: what's the details of 'scale offset'?
- Can you please provide the train code?
- Seems like there existed obvious errors in the utils.py HOT 1
- what the function get_transformed_corner() mean? HOT 1
- can you provide the train code
- About the ratio of SR-RAW dataset
- How can we test your provided model using PNG file? OR how can we convert .PNG image to .ARW?
- Dataset and code licence
- About PSNR, SSIM and LPIPS on the released model HOT 2
- iPhoneX-DSLR data
- Generalization and calculate PSNR
- Queries about the dataset
- Can't make inference work on CR2 or DNG raw data
- Can you please provide the crop information for aligning the field of view of the different images?
- About spatial loss calculation is wrong?
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from zoom-learn-zoom.