PyTorch implementation of PISR

This is an official implementation of the paper "Learning with Privileged Information for Efficient Image Super-Resolution", accepted to ECCV2020.

This work effectively boosts the performance of FSRCNN by exploiting a distillation framework, treating HR images as privileged information.

For more information, checkout the project site [website] and the paper [PDF].

Overview of our framework

Getting started

Dependencies

• Python 3.6
• PyTorch >= 1.1.0
• Please check requirements.txt

Docker

We provide a Dockerfile to reproduce our work easily.

$ docker build -t pisr:latest . # or docker pull wonkyunglee/pytorch_pisr:latest
$ docker run -it -v <working_dir>:/data -w /data pisr:latest /bin/bash

Datasets

• For training and validation
  • DIV2K
• For evaluation
  • Set5
  • Set14
  • B100
  • Urban100

Please download DIV2K dataset from here and other benchmark datasets from here.

After download all datasets, the folder data should be like this:

    data
    ├── benchmark
    │   ├── B100
    │   ├── Set14
    │   ├── Set5
    │   └── Urban100
    │       ├── HR
    │       └── LR_bicubic
    │           ├── X2
    │           ├── X3
    │           └── X4
    │      
    └── DIV2K
        ├── DIV2K_train_HR
        └── DIV2K_train_LR_bicubic
            ├── X2
            ├── X3
            └── X4

Training

First, clone our github repository.

$ git clone https://github.com/yonsei-cvlab/PISR.git

To train our teacher model, run the following script.

$ python step1_train_teacher.py --config configs/fsrcnn/step1.yml

To train our student model, run the following script.

$ python step2_train_student.py --config configs/fsrcnn/step2.yml

Using the pretrained models

• Download pre-trained weights for teacher model into results/fsrcnn/fsrcnn_teacher/checkpoint/ folder.
  Link: [weights]
• Download pre-trained weights for student model into results/fsrcnn/fsrcnn_student/checkpoint/ folder.
  Link: [weights]

Evaluation

To evaluate our student model, run following script. Benchmark datasets can be choosed by editing the config file configs/fsrcnn/base.ram.yml.

$ python evaluate.py --config configs/fsrcnn/step2.yml

---

Citation

@inproceedings{lee2020pisr,
    author={Lee, Wonkyung and Lee, Junghyup and Kim, Dohyung and Ham, Bumsub},
    title={Learning with Privileged Information for Efficient Image Super-Resolution},
    booktitle={Proceedings of European Conference on Computer Vision},
    year={2020},
}

---

Credit

Some parts of this code (e.g., data_loader) are based on EDSR-PyTorch repository.