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MSFS-Net

Multi-scale frequency separation network for image deblurring

Installation

Python 3.7.13
pytorch  1.9.0
CUDA 10.2
scikit-image
opencv-python
Tensorboard

Pretrained Models

We provide our pre-trained models. You can test our network according to the instruction below.

Baidu link:https://pan.baidu.com/s/1FwHEuyivhCP_BynZC0Ayjw password:0516

Google drive:

https://drive.google.com/drive/folders/1l0A8l1zqJJ6KOqNizQSFQIH3tksjOUMt?usp=sharing

weightstraining dataset
model.pklGoPro
model_R.pklRealBlur-R
model_J.pklRealBlur-J

Dataset

prepare datasets

GoPro

HIDE

RealBlur

Test

GoPro and HIDE

To test MSFS-Net,run the command below:

python main.py --model_name "MSFS-Net" --mode "test" --data_dir "dataset/GOPRO" --test_model "model.pkl"

note:You should change line 32 of main.py to model=build_net()

or to test MSFS-Net-Local, run the command below:

python main.py --model_name "MSFS-Net-Local" --mode "test" --data_dir "dataset/GOPRO" --test_model "model.pkl"

note:You should change line 32 of main.py to model=build_arch_net()

Output images will be saved in results/model_name/result_image folder.

RealBlur

The run command is the same as above,but you should change line 15 of main.py to from eval_R import _eval

PSNR and SSIM

We measured PSNR and SSIM using matlab functions.