https://github.com/kligvasser/xUnit

Learning a Spatial Activation Function for Efficient Image Restoration
https://github.com/kligvasser/xUnit

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Learning a Spatial Activation Function for Efficient Image Restoration

• Host: GitHub
• URL: https://github.com/kligvasser/xUnit
• Owner: kligvasser
• Created: 2018-01-04T12:58:44.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2021-03-21T08:08:18.000Z (over 3 years ago)
• Last Synced: 2024-08-02T11:18:59.365Z (4 months ago)
• Language: Python
• Size: 112 MB
• Stars: 88
• Watchers: 2
• Forks: 25
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# xUnit

Learning a Spatial Activation Function for Efficient Image Restoration.



  



Please refer our [paper](https://arxiv.org/abs/1711.06445) for more details.

## Citation

If you use this code for your research, please cite our papers:

```

@inproceedings{kligvasser2018xunit,

  title={xunit: Learning a spatial activation function for efficient image restoration},

  author={Kligvasser, Idan and Rott Shaham, Tamar and Michaeli, Tomer},

  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},

  pages={2433--2442},

  year={2018}

}

```

```

@article{kligvasser2018dense,

  title={Dense xUnit Networks},

  author={Kligvasser, Idan and Michaeli, Tomer},

  journal={arXiv preprint arXiv:1811.11051},

  year={2018}

}

```

## Code

### Clone repository

Clone this repository into any place you want.

```

git clone https://github.com/kligvasser/xUnit

cd xUnit

```

### Install dependencies

```

python -m pip install -r requirements.txt

```

This code requires PyTorch 1.0+ and python 3+.

### Super-resoltution

Pretrained models are avaible at: [LINK](https://www.dropbox.com/s/hq1n5yrl5hjsh34/sr_pretrained.zip?dl=0).



  



#### Dataset preparation

For the super-resolution task, the dataset should contains a low and high resolution pairs, in folder structure of:

```txt

train

├── img

├── img_x2

├── img_x4

val

├── img

├── img_x2

├── img_x4

```

You may prepare your own data by using the matlab script:

```

./super-resolution/scripts/matlab/bicubic_subsample.m

```

Or download a prepared dataset based on the [BSD](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/) and [VOC](http://host.robots.ox.ac.uk/pascal/VOC/) datasets from [LINK](https://www.dropbox.com/s/o1nzpr9q7vup8b7/bsdvoc.zip?dl=0).

#### Train xSRGAN x4 PSNR model

```

python3 main.py --root  --g-model g_xsrgan --d-model d_xsrgan --model-config "{'scale':4, 'gen_blocks':10, 'dis_blocks':5}" --scale 4 --reconstruction-weight 1.0 --perceptual-weight 0 --adversarial-weight 0 --crop-size 40

```

#### Train xSRGAN x4 WGAN-GP model

```

python3 main.py --root  --g-model g_xsrgan --d-model d_xsrgan_ad --model-config "{'scale':4, 'gen_blocks':10, 'dis_blocks':5}" --scale 4 --reconstruction-weight 1.0 --perceptual-weight 1.0 --adversarial-weight 0.005 --crop-size 64 --epochs 1200 --step-size 900 --gen-to-load  --wgan --penalty-weight 10

```

#### Train xSRGAN x4 with SN-discriminator model

```

python3 main.py --root  --g-model g_xsrgan --d-model d_xsrgan --model-config "{'scale':4, 'gen_blocks':10, 'dis_blocks':5, 'spectral':True}" --scale 4 --reconstruction-weight 1.0 --perceptual-weight 1.0 --adversarial-weight 0.01 --crop-size 40 --epochs 2000 --step-size 800 --gen-to-load  --dis-betas 0 0.9

```

#### Eval xSRGAN x4 model

```

python3 main.py --root  --g-model g_xsrgan --d-model d_xsrgan --model-config "{'scale':4, 'gen_blocks':10, 'dis_blocks':5}" --scale 4 --evaluation --gen-to-load 

```

### Gaussian denoising

Pretrained models are avaible at: [LINK](https://www.dropbox.com/s/zychmfzx52y8tvq/denoising_pretrained.zip?dl=0).

#### Dataset preparation

For the denoising task, the dataset should contains only clean images, in folder structure of:

```txt

train

├── img

val

├── img

```

#### Train xDNCNN Grayscale 50 sigma PSNR model

```

python3 main.py --root  --g-model g_xdncnn --d-model d_xdncnn --model-config "{'gen_blocks':10, 'dis_blocks':4, 'in_channels':1}" --reconstruction-weight 1.0 --perceptual-weight 0 --adversarial-weight 0 --crop-size 50 --gray-scale --noise-sigma 50 --epochs 500 --step-size 150

```

#### Train xDNCNN 75 sigma PSNR model

```

python3 main.py --root  --g-model g_xdncnn --d-model d_xdncnn --model-config "{'gen_blocks':10, 'dis_blocks':4, 'in_channels':3}" --reconstruction-weight 1.0 --perceptual-weight 0 --adversarial-weight 0 --crop-size 64 --noise-sigma 75 --epochs 1000 --step-size 300

```

#### Train xDNCNN 75 sigma WGAN-GP model

```

python3 main.py --root  --g-model g_xdncnn --d-model d_xdncnn --model-config "{'gen_blocks':10, 'dis_blocks':4, 'in_channels':3}" --reconstruction-weight 1.0 --perceptual-weight 1.0 --adversarial-weight 0.01 --crop-size 72 --noise-sigma 75 --epochs 1000 --step-size 300 --gen-to-load  --wgan --penalty-weight 10

```

#### Train xDNCNN Grayscale blind PSNR model

```

python3 main.py --root  --g-model g_xdncnn --d-model d_xdncnn --model-config "{'gen_blocks':10, 'dis_blocks':5, 'in_channels':1}" --reconstruction-weight 1.0 --perceptual-weight 0 --adversarial-weight 0 --crop-size 50 --gray-scale --noise-sigma 50 --blind --epochs 500 --step-size 150

```