Morteza Ghahremani, Mohammad Khateri, Alejandra Sierra, Jussi Tohka

AiVi, UEF, Finland

This repository is the official implementation of ADL: Adversarial Distortion Learning for denoising medical and computer vision images (arxiv, supp, pretrained models, visual results).

TensorFlow	PyTorch

ADL achieves state-of-the-art Gaussian denoising performance in

• grayscale/color image denoising in Medical imaging 🔥🔥🔥
• grayscale/color image denoising in Computer Vision images 🔥🔥🔥
• JPEG compression artifact reduction 🔥🔥🔥
• grayscale/color deblurring 🔥🔥🔥

• Proposed Efficient-UNet (Denoiser)

• Proposed Efficient-UNet (Discriminator)

• Results on the BSD68 dataset for Additive white Gaussian noise:

• Here we reported the results of the techniques reported by the authors.
• Our ADL was trained on the grey Flickr2K dataset only!

CBSD68 (img_id: test015)	Noisy (σ=25)	SwinIR	ADL (ours)

• Results on the CBSD68 dataset for Additive white Gaussian noise:

CBSD68 (img_id: test015)	Noisy (σ=50)	SwinIR	ADL (ours)

If you find ADL useful in your research, please cite our tech report:

@article{ADL2022,
    author = {Morteza Ghahremani, Mohammad Khateri, Alejandra Sierra, Jussi Tohka},
    title = {Adversarial Distortion Learning for Medical Image Denoising},
    journal = {arXiv:2204.14100},
    year = {2022},
}