Here is a fully trained model of EDSR (Enhanced Deep Residual Networks for Single Image Super-Resolution) model. This model surpassed the performance of the current available SOTA models.

Paper Link - https://arxiv.org/pdf/1707.02921

Keras Example link - https://keras.io/examples/vision/edsr/

It was trained for 500 epochs with 200 steps each.

TODO:

Hack to make this work for any image size. Currently the model takes input of image size 150 x 150.
We pad the input image with transparant pixels so that it is a square image, which is a multiple of 150 x 150
Then we chop the image into multiple 150 x 150 sub images
Upscale it and stich it together.

The output image might look a bit off, because each sub-image dosent have data about other sub-images.
This approach assumes that the subimage has enough data about its surroundings