1. Download the dataset files from the Google Drive
2. Unzip the files to the corresponding folders. The structure tree of the unzipped folders is as follows:

   .
   ├── ALL_HR
   |── ALL_mask
   |── valid
   |   ├── defocus
   |   |   ├── LR
   |   |   |   └── X4
   |   └── motion
   |       ├── LR
   |       |   └── X4
   |── area_class.npy
   |── degree_class.npy
   |── defocus_motion_class.npy
   |── train_validation.npy
   └── train_validation_split.py
   

   File description for the unzipped folder:

   • README.md: This file.

   • ALL_HR: The high-resolution images of the ReBlurSR dataset, including the ReBlurSR-Train and ReBlurSR-Test subsets.

   • ALL_mask: The blur map for the HR images in ALL_HR. For each sample, the blur map value is 0(0) if the pixel is blurred, and 1(255) if the pixel is non-blurred.

   • valid: contains the LR versions of the ReBlurSR-Test subset. defocus and motion subfolders contain the defocus and motion subsubsets of the ReBlurSR-Test subset, respectively. Its structure tree is as follows:

     valid
     ├── defocus
     │   ├── LR
     │   │   └── X4
     └── motion
         └── LR
            └── X4
     

   • area_class.npy: The category of the area of the blur region in the ReBlurSR-Test subset. Samples are divided into 3 classes: small, medium, and large.

         0:"large",      1:"medium",     2:"small
     

   • degree_class.npy: The category of the degree of the blur region in the ReBlurSR-Test subset. Samples are divided into 3 classes: heavy, little, and middle.

         0:"heavy",      1:"little",     2:"middle"
     

   • defocus_motion_class.npy: The category of the blur type in the ReBlurSR-Test subset. Samples are divided into 2 classes: defocus and motion.

         0:"defocus",    1:"motion"
     

   • train_validation.npy: The category of the samples in the ReBlurSR-Train and ReBlurSR-Test subsets. Samples are divided into 2 classes: train and validation.

         0:"train",      1:"validation"
     

   • train_validation_split.py: The script to generate the ReBlurSR-Train and ReBlurSR-Test subsets from ALL_HR and ALL_mask folders according to the defocus_motion_class.npy and train_validation.npy files.

3. run the train_validation_split.py script to generate the ReBlurSR-Train and ReBlurSR-Test subsets.

   python train_validation_split.py # required packages: numpy, tqdm

4. The generated ReBlurSR-Train and ReBlurSR-Test subsets are saved in the train and valid folders, respectively. The structure tree of the complete train folder and valid folder are as follows:

   train
   ├── motion
   │   ├── HR
   │   └── mask
   └── defocus
       ├── HR
       └── mask
   
   valid
   ├── motion
   │   ├── HR
   │   ├── LR
   |   |   └── X4
   │   └── mask
   └── defocus
       ├── HR
       ├── LR
       |   └── X4
       └── mask
   

You can generate the subsets of the ReBlurSR-Test according to the area_class.npy, degree_class.npy, and defocus_motion_class.npy files.