DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
Code for this paper DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
Orest Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang
In ICCV 2019
Overview
We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named
DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility. DeblurGAN-v2
is based on a relativistic conditional GAN with a double-scale discriminator. For the first time, we introduce the
Feature Pyramid Network into deblurring, as a core building block in the generator of DeblurGAN-v2. It can flexibly
work with a wide range of backbones, to navigate the balance between performance and efficiency. The plug-in of
sophisticated backbones (e.g., Inception-ResNet-v2) can lead to solid state-of-the-art deblurring. Meanwhile,
with light-weight backbones (e.g., MobileNet and its variants), DeblurGAN-v2 reaches 10-100 times faster than
the nearest competitors, while maintaining close to state-of-the-art results, implying the option of real-time
video deblurring. We demonstrate that DeblurGAN-v2 obtains very competitive performance on several popular
benchmarks, in terms of deblurring quality (both objective and subjective), as well as efficiency. Besides,
we show the architecture to be effective for general image restoration tasks too.
DeblurGAN-v2 Architecture
Datasets
The datasets for training can be downloaded via the links below:
Training
Command
python train.py
training script will load config under config/config.yaml
Tensorboard visualization
Testing
To test on a single image,
python predict.py IMAGE_NAME.jpg
By default, the name of the pretrained model used by Predictor is ‘best_fpn.h5’. One can change it in the code (‘weights_path’ argument). It assumes that the fpn_inception backbone is used. If you want to try it with different backbone pretrain, please specify it also under [‘model’][‘g_name’] in config/config.yaml.
Pre-trained models
| Dataset |
G Model |
D Model |
Loss Type |
PSNR/ SSIM |
Link |
| GoPro Test Dataset |
InceptionResNet-v2 |
double_gan |
ragan-ls |
29.55/ 0.934 |
fpn_inception.h5 |
| MobileNet |
double_gan |
ragan-ls |
28.17/ 0.925 |
fpn_mobilenet.h5 |
| MobileNet-DSC |
double_gan |
ragan-ls |
28.03/ 0.922 |
|
Parent Repository
The code was taken from https://github.com/KupynOrest/RestoreGAN . This repository contains flexible pipelines for different Image Restoration tasks.
Citation
If you use this code for your research, please cite our paper.
```
@InProceedings{Kupyn_2019_ICCV,
author = {Orest Kupyn and Tetiana Martyniuk and Junru Wu and Zhangyang Wang},
title = {DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2019}
}
```
评论