Qiongshuai Lyu, Min Guo, Zhao Pei, DeGAN: Mixed noise removal via generative adversarial networks, Applied Soft Computing, Volume 95, 2020, 106478, ISSN 1568-4946,
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three types of noise
- White Gaussian noise(AWGN)
- Salt-and-Paper noise(SPIN)
- random-valued noise(RVIN)
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Training Data
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Dataset in this paper
- Joseph Chet Redmon, Pascal VOC Dataset Mirror (VOC2007).
- K. Zhang, Datasets, 2016.
https://drive.google.com/drive/u/0/folders/0B-_yeZDtQSnobXIzeHV5SjY5NzA
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Our noise dataset
I change the unet, because the paper's information can not match the paper's picture, so i use the most common unet to my generator. And I also change the loss function, because I thought that the ssim loss is more important than mse, so I give some different weight to different loss.
- numpy==1.19.2
- Pillow==8.1.0
- torch==1.7.1+cu110
- torchaudio==0.7.2
- torchvision==0.8.2+cu110
- typing-extensions==3.7.4.3
pip install -r requirements.txt
PSNR = 69.36
SSIM = 0.746
noise image
denoise image
ground truth
noise distribution only for case-by-case