Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations
這篇文章首先介紹了現有現有超分網絡大多通過利用HR圖像直接進行雙三次插值下采樣得到LR圖像來構成數據集，但使用這種方式構造出的數據集訓練出的網絡，對真實世界的含模糊核和噪聲的低分辨率圖片，泛化能力較差，生成的HR圖像質量不佳，因此人們又提出在下采樣構造lr圖像的同時引入模糊核和噪聲，但這種方式構造的數據集訓練的網絡也只能泛化幾種模糊核和噪聲的組合仍無法泛化真實世界的圖片， Learning a S
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相關文章
- 1. SRMD:Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
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