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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.

About

Structure-Preserving Image Super-Resolution via Contextualized Multitask Learning, T-MM 2017.

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