Methods › Computer Vision › Image Denoising Models › DU-GAN
DU-GAN
Introduced by Zhizhong Huang et al. in DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
DU-GAN is a generative adversarial network for LDCT denoising in medical imaging. The generator produces denoised LDCT images, and two independent branches with U-Net based discriminators perform at the image and gradient domains. The U-Net based discriminator provides both global structure and local per-pixel feedback to the generator. Furthermore, the image discriminator encourages the generator to produce photo-realistic CT images while the gradient discriminator is utilized for better edge and alleviating streak artifacts caused by photon starvation.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising 24 Aug 2021 · 1 repository · arXiv:2108.10772
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Denoising | 1 |
| Diagnostic | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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