Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers paper, supplementary file

By Jingwen He, Chao Dong, and Yu Qiao

class AdaptiveFM(nn.Module):
    def __init__(self, in_channel, kernel_size):
        super(AdaptiveFM, self).__init__()
        padding = (kernel_size - 1) // 2
        self.transformer = nn.Conv2d(in_channel, in_channel, kernel_size, padding=padding, groups=in_channel)

    def forward(self, x):
        return self.transformer(x) + x

BibTex

@InProceedings{He_2019_CVPR,
author = {He, Jingwen and Dong, Chao and Qiao, Yu},
title = {Modulating Image Restoration With Continual Levels via Adaptive Feature Modification Layers},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

Dependencies

• Python 3 (Recommend to use Anaconda)
• PyTorch >= 0.4.0
• NVIDIA GPU + CUDA
• Python packages: pip install numpy opencv-python lmdb
• [option] Python packages: pip install tensorboardX, for visualizing curves.

Pretrained models

We provide a pretrained model for AdaFM-Net (experiments/pretrained_models) that deals with denoising from σ15 to σ75. Please run the following commands directly:

cd codes
python interpolate.py -opt options/test/test.json

The results can be found in the newly created directory AdaFM/results The noise level of the input image is σ45, and you are supposed to obtain similar interpolated results as follows:

Codes

The overall code framework mainly consists of four parts - Config, Data, Model and Network. We also provides some useful scripts. Please run all the following commands in “codes” directory.

How to Test

basic model and AdaFM-Net

1. Modify the configuration file options/test/test.json (please refer to options for instructions.)
2. Run command:

python test.py -opt options/test/test.json

modulation testing

1. Modify the configuration file options/test/test.json
2. Run command:

python interpolate.py -opt options/test/test.json

or:

1. Use scripts/net_interp.py to obtain the interpolated network.
2. Modify the configuration file options/test/test.json and run command: python test.py -opt options/test/test.json

How to Train

basic model

1. Prepare datasets, usually the DIV2K dataset. More details are in codes/data.
2. Modify the configuration file options/train/train_basic.json (please refer to options for instructions.)
3. Run command:

python train.py -opt options/train/train_basic.json

AdaFM-Net

1. Prepare datasets, usually the DIV2K dataset.
2. Modify the configuration file options/train/train_adafm.json
3. Run command:

python train.py -opt options/train/train_adafm.json

Acknowledgement

• This code borrows heavily from BasicSR.