# SPNet

**Repository Path**: hsi_chen/SPNet

## Basic Information

- **Project Name**: SPNet
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-01-15
- **Last Updated**: 2021-01-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](http://mmlab.ie.cuhk.edu.hk/projects/FSRCNN.html) 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](http://cvlab.postech.ac.kr/~mooyeol/pascal_voc_2012/) and [boundary map](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/) 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](https://github.com/pytorch/examples), thanks for their great work.