Most current super-resolution (SR) reconstruction methods suffer from edge blurring and insufficient detail reconstruction. To avoid these problems, this paper proposes a reference-based texture enhancement network for single image SR (RTEN-SR). Firstly, a preliminary reconstruction module (PRM) is constructed to learn the initial reconstructed high-resolution (HR) image features. Then, a multi-scale texture enhancement module (MTEM) based on the idea of texture transfer is designed to further supplement and enhance the texture details for the initial reconstructed HR images. To improve the feature learning ability of network, an efficient mixed attention module (EMAM) is constructed by combining channel attention and spatial attention. Meanwhile, the EMAM is integrated into the above two modules to enhance the important image features in the channel and spatial dimensions. Experimental results demonstrate that the proposed method can achieve better performance than state-of-the-art methods, effectively improving the objective indexes and visual results of reference-based image super-resolution (RefSR). For example, the proposed method outperforms the best RefSR method among the comparison methods by 0.38 dB in PSNR metric and 0.128 in SSIM metric.

中文翻译：

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RTEN-SR：基于参考的单图像超分辨率纹理增强网络

当前大多数超分辨率（SR）重建方法都存在边缘模糊和细节重建不足的问题。为了避免这些问题，本文提出了一种基于参考的单图像SR纹理增强网络（RTEN-SR）。首先，构建初步重建模块（PRM）来学习初始重建的高分辨率（HR）图像特征。然后，设计了基于纹理迁移思想的多尺度纹理增强模块（MTEM），以进一步补充和增强初始重建HR图像的纹理细节。为了提高网络的特征学习能力，结合通道注意力和空间注意力构建了高效的混合注意力模块（EMAM）。同时，EMAM被集成到上述两个模块中，以增强通道和空间维度上的重要图像特征。实验结果表明，该方法可以取得比最先进方法更好的性能，有效提高基于参考的图像超分辨率（RefSR）的客观指标和视觉效果。例如，所提出的方法在 PSNR 指标上优于比较方法中最好的 RefSR 方法 0.38 dB，在 SSIM 指标上优于 0.128 dB。