SPNet

This is pytorch implementation of T-MM17 paper Structure-Preserving Image Super-Resolution via Contextualized Multitask Learning. In this work, we proposed a efficient and structure-preserving image super-resolution framework by incorporating light-weight architecture and contextualized learning.

Prerequisites

• Computer with Linux
• Pytorch 0.3.0
• A NVIDIA GPU with CUDA8.0 installed

Data Generation

We put Set14 at ./Train, you should download General-100 and put it at ./Train/General-100 as well. First, we should run scripts generate_train.m and generate_test.m to generate sub-images.

Evaluation

In this implemention, we evaluate SPNet on Set14. In addition, we also provide a baseline model(e.g. FSRCNN) for better comparison. Both of us were trained on General-100 with 1000 epoches. The training code are main_spnet.py and main_cnn_baseline.py, respectively.

The proposed model achieve well balance beween efficiency and performance. Runing the evaluation script with sh eval.sh, with the output as:

=========SPNet==========
The testing time is 0.837336 second
Avg. PSNR: 28.7753 dB   Bilinear 27.1091 dB 
========================

=========Baseline==========
The testing time is 0.920399 second
Avg. PSNR: 28.5425 dB   Bilinear 27.1091 dB 
========================

Since the boundary contextualized model requires lots of manual efforts and the training process is too complex to provide one-step script. Thus, we provide a model trained with VOC2012 and boundary map in ./checkpoints/main_spnetmodel_pre_trained.pth. You can run script sh eval_pre_trained.sh to re-produce our results with output as:

=========SPNet==========
The testing time is 0.814436 second
Avg. PSNR: 29.2629 dB   Bilinear 27.1091 dB 
========================

Train

We have organized the training code for RCN and BCN components. You can train the model by using the following command:

python main_spnet.py

Feedback and Citation

If SPNet helps in your research, you can cite our paper:

@article{Shi2017Structure,
  title={Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning},
  author={Shi, Yukai and Wang, Keze and Chen, Chongyu and Xu, Li and Lin, Liang},
  journal={IEEE Transactions on Multimedia},
  volume={PP},
  number={99},
  pages={1-1},
  year={2017},
}

Also, if you have any question, please feel free to contact me by sending mail to shiyk3ATmail2.sysu.edu.cn.

Acknowledgement

This code is heavily rely on pytorch examples, thanks for their great work.