VGG19 for grayscale images about fast-srgan HOT 1 CLOSED

Hey Mustaeen,

A simple workaround for the grayscale problem would be to convert your grayscale images to RGB (using PIL or opencv). Then you can use everything as is. I'm not sure if it would have an impact on how the vgg model outputs features as like you mentioned, it wasn't trained on grayscale images. You can look at fine tuning an existing vgg model on grayscale images too (download a dataset for classification, convert images to grayscale using PIL or OpenCV and then train that) and then you can use that VGG model for super resolution. I would assume this would boost the performance of SR on grayscale images.

For your second question, I haven't done much work with super resolution on 3D volumes. But assuming your volumetric data can be sliced up into 2D images, you can use the 2D slices to train existing SR models. If you want to go further, you'll have to introduce 3D convolutions into the code and model the problem correctly.

from fast-srgan.