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GitHub - Holmes-Alan RefVAE: Reference based Image Super-Resolution via . . . For proposed RefVAE model, we claim the following points: • First working on using Variational AutoEncoder for reference based image super-resolution • Our proposed RefVAE can expand the SR space so that multiple SR images can be generated
Variational AutoEncoder for Reference based Image Super-Resolution In this paper, we propose a novel reference based image super-resolution approach via Variational AutoEncoder (RefVAE) Existing state-of-the-art methods mainly focus on single image super-resolution which cannot perform well on large upsampling factors, e g , 8 ×
Variational AutoEncoder for Reference based Image Super-Resolution We propose RefVAE to explicitly discover image dis-tribution using the proposed Conditional Variational AutoEncoder for image super-resolution Instead of learning directly the data distribution, we introduce references as a condition to guide super-resolution
Variational AutoEncoder for Reference based Image Super-Resolution Abstract: In this paper, we propose a novel reference based image super-resolution approach via Variational AutoEncoder (RefVAE) Existing state-of-the-art methods mainly focus on single image super-resolution which cannot perform well on large upsampling factors, e g , 8×
苏黎世联邦理工学院 | 具有可变形注意Transformer 的基于参考 . . . 代码链接:基于参考的图像超分辨率（RefSR）旨在利用辅助参考（Ref）图像来超分辨率低分辨率（LR）图像。因为它提供了一种超越单图像 SR 的替代方法，(ii) 如何从参考图像中转移相关纹理以补偿 LR 图像的细节是非常具有挑战性的，每个尺度由一个纹理特征编码器（TFE）模块、一个基于参考的可变形
VariationalAutoEncoderforReferencebasedImageSuper-Resolution Hence, we propose a reference based image super-resolution (RefVAE), which can search similar pat- ternsfromthereference toguidethesuper-resolution It is an online searching but fast processing because of the propsoed Conditional Variational Inference (CVAE)