讀論文：Image Super-Resolution by Neural Texture Transfer
文章：https://arxiv.org/abs/1903.00834 源碼：https://github.com/ZZUTK/SRNTT 1 介紹 論文主要貢獻： 1.一個更普遍的RefSR問題，打破了SISR的性能障礙（即缺乏紋理細節），並放寬了現有RefSR的約束（即一致性假設）。 2.對於RefSR問題，提出了端到端深度模型SRNTT，以通過多尺度神經紋理傳遞恢復以任何給定參考爲條件的LR
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相關文章
- 1. Image Super-Resolution by Neural Texture Transfer
- 2. Image Super-Resolution by Neural Texture Transfer(CVPR2019)
- 3. [論文筆記] Image Super-Resolution by Neural Texture Transfer (CVPR2019)
- 4. [論文閱讀]Image Quilting for Texture Synthesis and Transfer
- 5. 經典論文重讀---風格遷移篇(一)：Image Style Transfer Using Convolutional Neural Networks
- 6. StyleBank: An Explicit Representation for Neural Image Style Transfer 論文理解
- 7. 《image Style Transfer Using Convolutional Neural Networks》論文筆記
- 8. 論文閱讀 《Multimodal Remote Sensing Image Registration Based on Image Transfer and Local Features》
- 9. [Style Transfer]——Neural Style Transfer: A Review
- 10. Visual Attribute Transfer through Deep Image Analogy論文閱讀筆記