article: Blind2Unblind

@InProceedings{Wang_2022_CVPR,
    author    = {Wang, Zejin and Liu, Jiazheng and Li, Guoqing and Han, Hua},
    title     = {Blind2Unblind: Self-Supervised Image Denoising With Visible Blind Spots},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {2027-2036}
}

The original code is placed here: github

The model is built in Python3.8.5, PyTorch 1.7.1 in Ubuntu 22.04 environment.

Please put your training dataset under the path: ./b2u_sber_implemetation/data/train.

​ Please put your validation dataset under the path: ./b2u_sber_implemetation/data/test.

You can find pre-trained models here: ./b2u_sber_implemetation/pretrained_models

Models were trained on datasets G-209, Crystal_focus_0_dose_180, G-146

# # For more noisy datasets processing use model firstly trained on G-209
./pretrained_models/b2u_first.pth
# Than use model secondly trained on G-209 denoised by first model
./pretrained_models/b2u_second.pth


# # For less noisy images use model trained on Crystal_focus_0_dose_180
./pretrained_models/b2u_crystal_first.pth

• For training your own model please use SBER_train

Please put your test data in the folder: ./b2u_sber_implemetation/test

• To test model on images maximum size 768x1024 use SBER_test_small_images

• To test model on large resolution images use SBER_test_large_images

In this jupyter notebook you can set:

• your image proportions,
• crop propotions
• margin value for cropping and concating without visible joints