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CFSNet: Toward a Controllable Feature Space for Image Restoration ICCV2019
时间 2020-12-30
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深度学习
图像超分
python
ICCV
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Python
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CFSNet: Toward a Controllable Feature Space for Image Restoration 图1:图像超分辨率(第一行)的感知失真权衡与噪声降低与细节保留(第二行)的权衡,在测试时间,使用CFSNet时,用户可以很容易地根据个人喜好调整输入控制变量达到最满意的结果。相比之下,固定的方法(如EDSR、dncn - b)并不能总是保证最佳的视觉质量。 摘要 深
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