EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Enhanced Deep Residual Networks for Single Image Super-Resolution by Lim et al. (2017) and first released in this repository. The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and EDSR upscaling x2. This is a base model (~5mb vs ~100mb) that includes just 16 ResBlocks and 64 channels. You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset. The model can be used with the super_image library:

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