thangvubk / PESR

Official code (Pytorch) for paper Perception-Enhanced Single Image Super-Resolution via Relativistic Generative Networks

PESR

Official implementation for Perception-Enhanced Single Image Super-Resolution via Relativistic Generative Networks ECCV Workshop 2018

Citation

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@InProceedings{Vu_2018_ECCV_Workshops},
author = {Vu, Thang and Luu, Tung M. and Yoo, Chang D.},
title = {Perception-Enhanced Image Super-Resolution via Relativistic Generative Adversarial Networks},
booktitle = {The European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}

Dependencies

• Nvidia GPUs (training takes 1 day on 4 Titan Xp GPUs)
• At least 16G RAM
• Python3
• Pytorch 0.4
• tensorboardX
• tqdm
• imageio

Datasets, models, and results

Dataset

• Train: DIV2K (800 2K-resolution images)
• Valid (for visualization): DIV2K (100 val images), PIRM (100 self-val images)
• Test: Set5, Set14, B100, Urban100, PIRM (100 self-val images), DIV2K (100 val images)
• Download train+val+test datasets
• Download test only dataset

Pretrained models

• Download pretrained models including 1 PSNR-optimized model and 1 perception-optimized model

Paper results

• Download paper results in images of the test datasets

Quick start

• Download test dataset and put into data/origin/ directory
• Download pretrained model and put into check_point directory
• Run python test.py --dataset <DATASET_NAME>
• Results will be saved into results/ directory

Training

• Download train+val+test dataset and put into data/origin directory
• Pretrain with L1 loss: python train.py --phase pretrain --learning_rate 1e-4
• Finetune on pretrained model with GAN: python train.py
• Models with be saved into check_point/ direcory

Visualization

• Start tensorboard: tensorboard --logdir check_point
• Enter: YOUR_IP:6006 to your web browser.
• Tensorboard when finetuning on pretrained model should be similar to:

Comprehensive testing

• Test perceptual model: follow Quick start
• Interpolate between perceptual model and PSNR model: python test.py --dataset <DATASET> --alpha <ALPHA> (with alpha being perceptual weight)
• Test perceptual quality: refer to PIRM validation code

Quantitative and Qualitative results

RED and BLUE indicate best and second best respectively.

References

• EDSR-pytorch
• Relativistic-GAN