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  # Accelerating Image Super-Resolution Networks with Pixel-Level Classification
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  [![Project Page](https://img.shields.io/badge/Project-Page-green)](https://3587jjh.github.io/PCSR/)
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  [![arXiv](https://img.shields.io/badge/arXiv-2407.21448-b31b1b)](https://arxiv.org/abs/2407.21448)
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  - NVIDIA GPU + CUDA<br>
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  - Python packages: `pip install numpy opencv-python pandas tqdm fast_pytorch_kmeans`
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- ## Demo
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- We offer a demo that can simulate 4x super-resolution using a single input image:
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- ```
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- python demo.py --img_path <image path> --k <value> [--adaptive] [--no_refinement]
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- ```
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- For a detailed description of the arguments and types of output, please refer to `demo.py` and the `results` directory.
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- <div style="display: flex;">
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- <figure style="margin: 0; text-align: center;">
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- <img src="results/PCSR_colored.png" style="width: 280px; height: 184px; object-fit: cover;"/>
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- <figcaption>Classification</figcaption>
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- </figure>
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- <figure style="margin: 0; text-align: center;">
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- <img src="results/PCSR.png" style="width: 280px; height: 184px; object-fit: cover;"/>
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- <figcaption>SR Result (X4)</figcaption>
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- </figure>
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- </div>
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-
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- ## Train
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- We offer a variety of pretrained models available in the `save` directory.<br>
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-
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- ### Data preprocessing
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- We use the DIV2K training set as our base.
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- ```
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- cd datasets/scripts
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- python data_augmentation.py
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- python extract_subimages.py
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- python generate_mod_LR_bic.py
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- ```
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- ### Run training scripts (example usage)
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- (Please move the pretrained model to a different directory to train a new model!)
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- ```
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- # original model
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- ./dist.sh train.py --config configs/carn-x4.yaml --gpu 0
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-
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- # PCSR model (2-stage)
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- ./dist.sh train_pcsr.py --config configs/carn-pcsr-phase0.yaml --gpu 0,1
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- ./dist.sh train_pcsr.py --config configs/carn-pcsr-phase1.yaml --gpu 0
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- ```
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-
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  ## Test
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  Please refer to `test.py` and `test_pcsr.py` for a detailed description of the arguments.
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  ```
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  primaryClass={cs.CV},
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  url={https://arxiv.org/abs/2407.21448},
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  }
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- ```
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-
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- ## Acknowledgement
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- This repo is based on [LIIF](https://github.com/yinboc/liif) and [KAIR](https://github.com/cszn/KAIR).
 
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+ ---
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+ tags:
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+ - super-resolution
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+ - Image-to-Image
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+ ---
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  # Accelerating Image Super-Resolution Networks with Pixel-Level Classification
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  [![Project Page](https://img.shields.io/badge/Project-Page-green)](https://3587jjh.github.io/PCSR/)
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  [![arXiv](https://img.shields.io/badge/arXiv-2407.21448-b31b1b)](https://arxiv.org/abs/2407.21448)
 
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  - NVIDIA GPU + CUDA<br>
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  - Python packages: `pip install numpy opencv-python pandas tqdm fast_pytorch_kmeans`
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  ## Test
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  Please refer to `test.py` and `test_pcsr.py` for a detailed description of the arguments.
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  ```
 
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  primaryClass={cs.CV},
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  url={https://arxiv.org/abs/2407.21448},
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  }
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+ ```