The study explored a deep learning image super-resolution approach which is commonly used in face recognition, video perception and other fields. These generative adversarial networks usually have high-frequency texture details. The relevant textures of high-resolution images could be transferred as reference images to low-resolution images. The latest existing methods use transformer ideas to transfer related textures to low-resolution images, but there are still some problems with channel learning and detailed textures. Therefore, the study proposed an enhanced texture transformer network (ETTN) to improve the channel learning ability and details of the texture. It could learn the corresponding structural information of high-resolution texture images and convert it into low-resolution texture images. Through this, finding the feature map can change the exact feature of images and improve the learning ability between channels. We then used multi-scale feature integration (MSFI) to further enhance the effect of fusion and achieved different degrees of texture restoration. The experimental results show that the model has a good resolution enhancement effect on texture transformers. In different datasets, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were improved by 0.1–0.5 dB and 0.02, respectively.

中文翻译：

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一种用于参考图像超分辨率的新型基于深度学习的增强纹理变换器网络

该研究探索了一种常用于人脸识别、视频感知等领域的深度学习图像超分辨率方法。这些生成对抗网络通常具有高频纹理细节。高分辨率图像的相关纹理可以作为参考图像转移到低分辨率图像。现有的最新方法使用transformer思想将相关纹理转移到低分辨率图像，但在通道学习和详细纹理方面仍然存在一些问题。因此，该研究提出了一种增强的纹理变换网络（ETTN）来提高纹理的通道学习能力和细节。它可以学习高分辨率纹理图像的相应结构信息，并将其转换为低分辨率纹理图像。通过这个，找到特征图可以改变图像的确切特征，提高通道间的学习能力。然后我们使用多尺度特征集成（MSFI）进一步增强融合的效果，实现了不同程度的纹理恢复。实验结果表明，该模型对纹理变换器具有良好的分辨率增强效果。在不同的数据集中，峰值信噪比 (PSNR) 和结构相似性 (SSIM) 分别提高了 0.1-0.5 dB 和 0.02。