SRDenseNet-PyTorch

Overview

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

Table of contents

• 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

Download weights

• Google Driver
• Baidu Driver

Download datasets

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.

How Test and Train

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

Test

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

Train SRDenseNet model

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

Resume train SRDenseNet model

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

Result

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.

Dataset	Scale	PSNR
Set5	4	32.02(31.71)
Set14	4	28.50(28.34)

# 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`

Credit

Image Super-Resolution Using Dense Skip Connections

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}
}