JaredYeDH / CVPR18-SFTGAN

Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform (CVPR 2018) http://mmlab.ie.cuhk.edu.hk/projects/SFTGAN/

CVPR18-SFTGAN [project page] [paper]

Torch implementation for Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform.

Training code will be available before June 10.

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😜 PyTorch - BasicSR  

We will provide Pytorch version of SFTGAN later (all will be available before June 10)

BasicSR contains basic codes for Super-Resolution. It now has provided basic SR models like SRResNet and SRGAN.

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Table of Contents

1. Introduction
2. Requirements and Dependencies
3. Test
4. Citation

Introduction

We have explored the use of semantic segmentation maps as categorical prior for SR.

A Spatial Feature Transform (SFT) layer has been proposed to efficiently incorporate the categorical conditions into a CNN network.

For more details, please check out our project webpage and paper.

Requirements and Dependencies

• Torch
• cuda & cudnn
• other torch dependencies, e.g. nngraph / paths / image (install them by luarocks install xxx)

Test

We test our model with Titan X/XP GPU.

1. Download segmentation model (OutdoorSceneSeg_bic_iter_30000.t7) and SFT-GAN model (SFT-GAN.t7) from google drive. Put them in the model folder.
2. There are 2 sample images in data/samples folder. You can put your images inside this folder.
3. Run th test_seg.lua The segmentation results are then in data/ with _segprob/_colorimg/_byteimg suffix.
4. Run th test_SFT-GAN.lua The results are then in data/ with prefix rlt_.

Citation

If you find the code and datasets useful in your research, please cite:

@inproceedings{wang2018sftgan,
    author = {Xintao Wang, Ke Yu, Chao Dong and Chen Change Loy},
    title = {Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform},
    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2018}

😆 Image Viewer

If you have trouble in comparing image details, may have a try for HandyViewer - an image viewer that you can switch image with a fixed zoom ratio.