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The Wasserstein distance is a key concept of the optimal transform\ntheory, and promises to improve the performance of the GAN. The perceptual loss\ncompares the perceptual features of a denoised output against those of the\nground truth in an established feature space, while the GAN helps migrate the\ndata noise distribution from strong to weak. Therefore, our proposed method\ntransfers our knowledge of visual perception to the image denoising task, is\ncapable of not only reducing the image noise level but also keeping the\ncritical information at the same time. Promising results have been obtained in\nour experiments with clinical CT images.","url_abs":"http://arxiv.org/abs/1708.00961v2","url_pdf":"http://arxiv.org/pdf/1708.00961v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"low-dose-ct-image-denoising-using-a","repo_url":"https://github.com/yyqqss09/ldct_denoising","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"low-dose-ct-image-denoising-using-a","repo_url":"https://github.com/Longer430/low_dose_CT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"low-dose-ct-image-denoising-using-a","repo_url":"https://github.com/Ryosaeba8/DLMI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"low-dose-ct-image-denoising-using-a","repo_url":"https://github.com/Ryosaeba8/Medical-Imaging-LOW-DOSE-CT-DENOISING","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"low-dose-ct-image-denoising-using-a","repo_url":"https://github.com/SSinyu/WGAN-VGG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"low-dose-ct-image-denoising-using-a","repo_url":"https://github.com/bme-2020/WGAN-VGG-_for_LDCT-Image-Denoising","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"low-dose-ct-image-denoising-using-a","repo_url":"https://github.com/daintlab/ct-denoising","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"low-dose-ct-image-denoising-using-a","repo_url":"https://github.com/hyeongyuy/CT-WGAN_VGG_tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"low-dose-ct-image-denoising-using-a","repo_url":"https://github.com/jaayeon/WGAN_VGG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-denoising","task_name":"Image Denoising"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.00961","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.00961"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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