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A Generative Adversarial Network-Based Image Denoiser Controlling Heterogeneous Losses
We propose a novel generative adversarial network (GAN)-based image denoising method that utilizes heterogeneous losses. In order to improve the restoration quality of the structural information of the generator, the heterogeneous losses, including the structural loss in addition to the conventional...
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| 出版年: | Sensors (Basel) |
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| 主要な著者: | , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI
2021
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7915760/ https://ncbi.nlm.nih.gov/pubmed/33567620 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s21041191 |
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