| 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 | |