Preliminary Preparation

1. Input images are put into dataset\haze, reference images are put into dataset\clear, UIEB test set images are put into dataset\test\UIEB, NYU-v2 test set images are put into dataset\test\UWCNN
2. The input image and the test image are named uniformly as dataset name_image parent number_sub number_sub number, for example UIEB_123_1_1, and the reference image is named uniformly as dataset name_image number, for example UIEB_123, and the dataset name_image parent number of the input image and the reference image must correspond, and the same for the test image.
3. Put the model (*.pth) into the snapshots folder
4. Modify the path information in the tran.py
   1. UIEB results folder: config.gen_UIEB_dir
   2. NUYv2 results folder: config.gen_UWCNN_dir
   3. Input image map: config.hazy_images_path
   4. Reference image set: config.orig_images_path
   5. UIEB test set: config.test_UIEB_dir
   6. NUYv2 test set: config.test_UWCNN_dir
   7. Model storage folder: config.snapshots_folder
5. Modify the path information in the test.py file

   1. Reference image set: real_file_path

   2. UIEB test set: UIEB_test_path

   3. NUYv2 test set: UWCNN_test_path

   4. UIEB results folder: UIEB_result_path

   5. NUYv2 results folder: UWCNN_result_path

   6. Model path: `modle_path

testing

python test.py

training

python train.py