Deep image prior
Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a gene...
| Main Authors: | , , |
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| 格式: | Journal article |
| 语言: | English |
| 出版: |
Springer
2020
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