Image Super-Resolution by Neural Texture Transfer(CVPR2019)
Motivation：基于参考图像的图像超分方法具有很大的潜力，但是当参考图像和LR图像不够相似时，或者未对齐时，图像的超分效果就会很差。 整体思路： 本文旨在利用来之参考图像的更多纹理信息来释放RefSR的潜力，即是在不提供相关的Ref图象时，也具有很强的鲁棒性。将RefSR问题形式化为神经纹理迁移问题，设计了端到端的模型，将Ref图像的纹理自适应的迁移，从而丰富HR图像细节，且在神经空间执行多
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相关文章
- 1. Image Super-Resolution by Neural Texture Transfer
- 2. 读论文：Image Super-Resolution by Neural Texture Transfer
- 3. [论文笔记] Image Super-Resolution by Neural Texture Transfer (CVPR2019)
- 4. Stereoscopic image quality assessment by deep convolutional neural network
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- 8. High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
- 9. Reading Note: Neural Aesthetic Image Reviewer
- 10. Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches 译文