{"url":"/method/du-gan","slug":"du-gan","name":"DU-GAN","full_name":"DU-GAN","full_name_withheld":false,"description_markdown":"**DU-GAN** is a [generative adversarial network](https://www.paperswithcode.com/methods/category/generative-adversarial-networks) for LDCT denoising in medical imaging. The generator produces denoised LDCT images, and two independent branches with [U-Net](https://paperswithcode.com/method/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.","description_state":"present","introduced_year":null,"introduced_by":{"title":"DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising","paper":"/paper/du-gan-generative-adversarial-networks-with","first_author":"Zhizhong Huang","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/du-gan-generative-adversarial-networks-with"},"source":{"url":"https://arxiv.org/abs/2108.10772v2","title":"DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Denoising Models","url":"/methods/category/image-denoising-models","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Adversarial Networks","url":"/methods/category/generative-adversarial-networks","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/du-gan-generative-adversarial-networks-with","title":"DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising","date":"2021-08-24","arxiv_id":"2108.10772","n_code_links":1,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/denoising","name":"Denoising","papers":1},{"task":"/task/diagnostic","name":"Diagnostic","papers":1}],"tasks_shown":2,"n_tasks":2,"usage_by_year":[{"year":"2021","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/du-gan"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}