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 |
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\ ADL achieves state-of-the-art Gaussian denoising performance in
Proposed Efficient-UNet (Denoiser)
Proposed Efficient-UNet (Discriminator)
σ | BM3D | WNNM | DnCNN | NLRN | FOCNet | MWCNN | DRUNet | SwinIR | ADL (ours) |
---|---|---|---|---|---|---|---|---|---|
15 | 31.08 | 31.37 | 31.73 | 31.88 | 31.83 | 31.86 | 31.91 | 31.97 | :fire: 32.11 :fire: |
25 | 28.57 | 28.83 | 29.23 | 29.41 | 29.38 | 29.41 | 29.48 | 29.50 | :fire: 29.50 :fire: |
50 | 25.60 | 25.87 | 26.23 | 26.47 | 26.50 | 26.53 | 26.59 | 26.58 | :fire: 26.87 :fire: |
CBSD68 (img_id: test015) | Noisy (σ=25) | SwinIR | ADL (ours) |
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σ | BM3D | WNNM | EPLL | MLP | CSF | TNRD | DnCNN | DRUNet | SwinIR | ADL (ours) |
---|---|---|---|---|---|---|---|---|---|---|
15 | 33.52 | 33.90 | 33.86 | 33.87 | 33.91 | - | 34.10 | 34.30 | 34.42 | :fire: 34.61 :fire: |
25 | 30.71 | 31.24 | 31.16 | 31.21 | 31.28 | 31.24 | 31.43 | 31.69 | 31.78 | :fire: 32.18 :fire: |
50 | 27.38 | 27.95 | 27.86 | 27.96 | 28.05 | 28.06 | 28.16 | 28.51 | 28.56 | :fire: 29.02 :fire: |
CBSD68 (img_id: test015) | Noisy (σ=50) | SwinIR | ADL (ours) |
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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},
}