This repository contains an op-for-op PyTorch reimplementation of Image Super-Resolution Using Dense Skip Connections.

• SRDenseNet-PyTorch
  • Overview
  • Table of contents
  • Download weights
  • Download datasets
  • Test
  • Train SRDenseNet model
  • Resume train SRDenseNet model
  • Result
  • Credit
    • Image Super-Resolution Using Dense Skip Connections

• Google Driver
• Baidu Driver

Contains DIV2K, DIV8K, Flickr2K, OST, T91, Set5, Set14, BSDS100 and BSDS200, etc.

• Google Driver
• Baidu Driver

Please refer to README.md in the data directory for the method of making a dataset.

Both training and testing only need to modify the config.py file.

Modify the config.py file.

• line 29: arch_name change to srdensenet_x4.
• line 33: upscale_factor change to 4.
• line 35: mode change to test.
• line 37: exp_name change to test_SRDenseNet_x4.
• line 82: model_weights_path change to ./results/pretrained_models/SRDenseNet_x4-ImageNet-bb28c23d.pth.tar.

python3 test.py

Modify the config.py file.

• line 29: arch_name change to srdensenet_x4.
• line 33: upscale_factor change to 4.
• line 35: mode change to train.
• line 37: exp_name change to SRDenseNet_x4.

python3 train.py

Modify the config.py file.

• line 29: arch_name change to srdensenet_x4.
• line 33: upscale_factor change to 4.
• line 35: mode change to train.
• line 37: exp_name change to SRDenseNet_x4.
• line 54: resume_model_weights_path change to ./samples/SRDenseNet_x4/epoch_xxx.pth.tar.

python3 train.py

Source of original paper results: https://openaccess.thecvf.com/content_ICCV_2017/papers/Tong_Image_Super-Resolution_Using_ICCV_2017_paper.pdf

In the following table, the value in () indicates the result of the project, and - indicates no test.

# Download `SRGAN_x4-ImageNet-c71a4860.pth.tar` weights to `./results/pretrained_models`
# More detail see `README.md<Download weights>`
python3 ./inference.py

Input:

Output:

Build `srdensenet_x4` model successfully.
Load `srdensenet_x4` model weights `./results/pretrained_models/SRDenseNet_x4-ImageNet-bb28c23d.pth.tar` successfully.
SR image save to `./figure/comic_sr.png`

Tong, Tong and Li, Gen and Liu, Xiejie and Gao, Qinquan

Abstract
Recent studies have shown that the performance of single-image super-resolution methods can be significantly boosted by using deep convolutional neural networks. In this study, we present a novel single-image super-resolution method by introducing dense skip connections in a very deep network. In the proposed network, the feature maps of each layer are propagated into all subsequent layers, providing an effective way to combine the low-level features and high-level features to boost the reconstruction performance. In addition, the dense skip connections in the network enable short paths to be built directly from the output to each layer, alleviating the vanishing-gradient problem of very deep networks. Moreover, deconvolution layers are integrated into the network to learn the upsampling filters and to speedup the reconstruction process. Further, the proposed method substantially reduces the number of parameters, enhancing the computational efficiency. We evaluate the proposed method using images from four benchmark datasets and set a new state of the art.

[Paper]

@inproceedings{tong2017image,
  title={Image super-resolution using dense skip connections},
  author={Tong, Tong and Li, Gen and Liu, Xiejie and Gao, Qinquan},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={4799--4807},
  year={2017}
}