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| title: EDSR Keras | |
| emoji: 🚀 | |
| colorFrom: pink | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: 3.18.0 | |
| python_version: 3.10.9 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| This space is the demo for the EDSR (Enhanced Deep Residual Networks for Single Image Super-Resolution) model. This model surpassed the performace of the current available SOTA models. | |
| Paper Link - https://arxiv.org/pdf/1707.02921 | |
| Keras Example link - https://keras.io/examples/vision/edsr/ | |
| 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 | |