Real-World Deep Local Motion Deblurring

Paper | Project Page | Video

ReLoBlur Dataset

ReLoBlur, the first real-world local motion deblurring dataset, which is captured by a synchronized beam-splitting photographing system. It consists of 2,405 blurred images with the size of 2,152×1,436 that are divided into 2,010 training images and 395 test images. For efficient training and testing, we also provide the resized version of ReLoBlur Dataset with the size of 538x359. ReLoBlur includes but is not limited to indoor and outdoor scenes of pedestrians, vehicles, parents and children, pets, balls, plants and furniture.

Data Download

	Baidu Netdisk	Google Drive	Number	Description
ReLoBlur test	link	link	395	We provide 395 pairs of testing images.
ReLoBlur train	link	link	2,010	We provide 2,010 pairs of training images.
Resized	link	link	2,405	We provide a resized version of ReLoBlur dataset. We resized ReLoBlur by BICUBIC algorithm.
Local Blur Mask	link	link	2,405	We provide a resized version of ReLoBlur dataset. We resized ReLoBlur by BICUBIC algorithm.

Important: ReLoBlur dataset can be only used for academic purposes!

LBAG: Local Blur-Aware Gated Network

Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring. LBAG detects blurred regions and restores locally blurred images simultaneously.

Environment

Before running LBAG, please install the environment on Linux:

pip install -U pip
pip install -r requirements.txt

Pre-processing

Put the training data and testing data under

LBAG/data/dataset/

and put the masks under

LBAG/data/

If you train or infer with your own data, the data structure should be like:

├── dataset
    ├── train
         ├── s01
             ├── 00
                ├── 00_sharp.png
                ├── 00_blur.png
                ├── ...
             ├── ...
         ├── s02
             ├── 00
             ├── ...
         ├── ...      
    ├── test
         ├── scene1
              ├── 00
              ├── 01
              ├── ...
         ├── scene2
              ├── 00
              ├── 01
              ├── ...
├── masks
    ├── train
         ├── s01
             ├── 00
                 ├── 00.png
                 ├── 01.png
                 ├── ...
             ├── 01
             ├── ...
         ├── s02
             ├── 00
             ├── ...
         ├── ...      
    ├── test
         ├── scene1
              ├── 00
              ├── 01
              ├── ...
         ├── scene2
              ├── 00
              ├── 01
              ├── ...

Pretrained Model

LBAG+ uses the pretrained model of MiMO_UNet, which could be downloaded here: https://github.com/chosj95/MIMO-UNet

And please put the pretrained model into

checkpoints/pretrained_mimounet/

You can infer LBAG by the pretrained model, which could be downloaded here:https://drive.google.com/drive/folders/1_T8Z7-T7i7BsaJhpq0rCNWLT65N_SslU?usp=sharing

Evaluation Code

You can evaluate the LBAG by using:

CUDA_VISIBLE_DEVICES=0 python LBAG_trainer.py --config configs/LBAG.yaml --exp_name 0223_munet_gate01_1 --reset --infer

Training

Training with single GPU

To train a model with your own data/model, you can edit the configs/LBAG.yaml and run the following codes:

CUDA_VISIBLE_DEVICES=0 python LBAG_trainer.py --config configs/LBAG.yaml --exp_name 0223_munet_gate01_1 --reset

#To do

Cication
Data on Baidu Netdisk
...