jkhu29 / deblurring-by-realistic-blurring

A PyTorch implementation of the "Deblurring by Realistic Blurring", unofficially

Deblurring by Realistic Blurring

article

two GANs are used, one for blurring the image(BGAN) and one for deblurring the image (DBGAN), with the former serving as a priori for the latter.

processes:

1. sharp --> bgan --> real blur
2. real blur --> dbgan --> sharp(fake)

losses：relativistic blur loss

the general loss is mainly to ensure that the GANs has the following effects:

1. the probability that the discriminator considers img to be the real category tends to be infinitely close to 1
2. the probability that the discriminator considers img to be the fake category tends to be infinitely close to 1

relativistic blur loss is to make p(fake_d) == p(real_d)

Data

please see this part in official implementation

but they do not have training script, that's why I write these code.

Model

BGAN：sharp --> blur

GBGAN：blur --> sharp, like DeblurGAN

BGAN

1. gaussian noise concat
2. Conv2d --> 9ResBlock --> 2Conv2d (maybe we could use less resblock)
3. ResBlock: 5Conv2d --> 4LeakyReLU (maybe we could use less Conv2d)
4. long res

GAN_D: vgg19, pretrained (without BN)

Because the data set is not aligned, the cyclegan idea is used

DBGAN

Basically the same as BGAN

1. without BN (why not IN)
2. 16个ResBlock (also, i don't think need so many resblocks)

Result

原图	模糊后	去模糊后

Usage

use tfrecord in pytorch

you can also see .h5 made in Chinese README

in ./dataset_make, run

python dataset_make.py --mode train_blur
python dataset_make.py --mode train_deblur 

then sh train_small.sh

TODOs

• format code
• amp in branch: amp
• visdom

Citation

@inproceedings{zhang2020deblurring,
  title={Deblurring by realistic blurring},
  author={Zhang, Kaihao and Luo, Wenhan and Zhong, Yiran and Ma, Lin and Stenger, Bjorn and Liu, Wei and Li, Hongdong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2737--2746},
  year={2020}
}