Awesome
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
weights | training dataset |
---|---|
model.pkl | GoPro |
model_R.pkl | RealBlur-R |
model_J.pkl | RealBlur-J |
Dataset
prepare datasets
GoPro
-
Download deblur dataset from the GoPro dataset.
-
Unzip files
dataset
folder. -
Preprocess dataset by running the command below:
python data/preprocessing.py
-
After preparing data set, the data folder should be like the format below:
GOPRO ├─ train │ ├─ blur % 2103 image pairs │ │ ├─ xxxx.png │ │ ├─ ...... │ │ │ ├─ sharp │ │ ├─ xxxx.png │ │ ├─ ...... │ ├─ test % 1111 image pairs │ ├─ ...... (same as train)
HIDE
-
Download deblur dataset from the HIDE dataset
-
Preprocess dataset by running the command below:
python data/HIDE.py
note: Please change the path of the dataset location in the code
format:the same as GoPro datasets
RealBlur
- Download deblur dataset from the RealBlur dataset
- The data folder should be like the format of GoPro datasets.
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.