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--- |
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tags: |
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- Keras |
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license: mit |
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metrics: |
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- PSNR |
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datasets: |
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- eugenesiow/Div2k |
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library_name: keras |
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pipeline_tag: image-to-image |
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--- |
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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. |
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Spaces link - https://huggingface.co/spaces/keras-io/EDSR |
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Paper Link - https://arxiv.org/pdf/1707.02921 |
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Keras Example link - https://keras.io/examples/vision/edsr/ |
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It was trained for 500 epochs with 200 steps each. |
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# Enhanced Deep Residual Networks for Single Image Super-Resolution |
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## Introduction |
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This repository contains a trained model based on the Enhanced Deep Residual Networks for Single Image Super-Resolution paper. The model was trained for 500 epochs with 200 steps each, resulting in a high-quality super-resolution model. |
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## Dataset Used |
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The model was trained on the DIV2K dataset, which is a newly proposed high-quality (2K resolution) image dataset for image restoration tasks. The DIV2K dataset consists of 800 training images, 100 validation images, and 100 test images. |
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## Architecture |
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The Enhanced Deep Residual Networks for Single Image Super-Resolution paper presents an enhanced deep super-resolution network (EDSR) and a new multi-scale deep super-resolution system (MDSR) that outperform current state-of-the-art SR methods. The EDSR model optimizes performance by analyzing and removing unnecessary modules to simplify the network architecture. The MDSR system is a multi-scale architecture that shares most of the parameters across different scales, using significantly fewer parameters compared with multiple single-scale models but showing comparable performance. |
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## Metrics |
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The model was evaluated using the PSNR (Peak Signal-to-Noise Ratio) metric, which measures the quality of the reconstructed image compared to the original image. The model achieved a PSNR of approximately 31, which is a high-quality result. |
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## TODO: |
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Hack to make this work for any image size. Currently the model takes input of image size 150 x 150. |
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We pad the input image with transparant pixels so that it is a square image, which is a multiple of 150 x 150 |
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Then we chop the image into multiple 150 x 150 sub images |
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Upscale it and stich it together. |
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The output image might look a bit off, because each sub-image dosent have data about other sub-images. |
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This approach assumes that the sub-image has enough data about its surroundings |
