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The mapping is represented as a deep convolutional neural network (CNN)\nthat takes the low-resolution image as the input and outputs the\nhigh-resolution one. We further show that traditional sparse-coding-based SR\nmethods can also be viewed as a deep convolutional network. But unlike\ntraditional methods that handle each component separately, our method jointly\noptimizes all layers. Our deep CNN has a lightweight structure, yet\ndemonstrates state-of-the-art restoration quality, and achieves fast speed for\npractical on-line usage. We explore different network structures and parameter\nsettings to achieve trade-offs between performance and speed. Moreover, we\nextend our network to cope with three color channels simultaneously, and show\nbetter overall reconstruction quality.","url_abs":"http://arxiv.org/abs/1501.00092v3","url_pdf":"http://arxiv.org/pdf/1501.00092v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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