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README.md
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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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## Train
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We offer a variety of pretrained models available in the `save` directory.<br>
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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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# 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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## 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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## 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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```
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