Abstract: Murals, as an important part of cultural heritage, have received widespread attention for their digital preservation and restoration in recent years. However, the super-resolution reconstruction of mural images often faces challenges such as texture blurring and the loss of original information. To address these issues, this paper proposes a Reference-based Two-stage Mural Image Super-Resolution Reconstruction (RTMISR) method. First, a multi-scale residual feature extraction module is employed to accurately capture the feature relationships between high-resolution and low-resolution mural images, ensuring the complete retention of low-resolution image information and achieving an initial reconstruction of mural contours and partial details. Then, a texture feature enhancement module is introduced, utilizing a coarse-to-fine feature matching method to extract high-quality texture information from reference images and effectively integrate it into the reconstructed images to enhance texture detail representation. Moreover, to ensure the relevance and quality of the reference images, a reference image selection module is designed to select reference images that are highly correlated with the target low-resolution image. Experimental results on mural datasets show that, compared to representative super-resolution methods such as SRGAN, MADNet, and ESRT, RTMISR achieves superior performance in objective metrics: for ×2 super-resolution, PSNR is improved by an average of 2.83 dB, and SSIM by 0.04; for ×4 super-resolution, PSNR is improved by an average of 2.00 dB, and SSIM by 0.02. In terms of subjective visual quality, RTMISR effectively retains the original information of murals while enhancing the texture details of mural images, achieving a better balance between model complexity and reconstruction performance.

摘要： 摘 要: 壁画作为重要的文化遗产，其数字化保护和修复在近年来得到了广泛关注。然而，在壁画图像的超分辨率重建过程中，往往面临纹理模糊和原有信息丢失的问题。针对这一问题，本文提出了一种基于参考的两阶段壁画图像超分辨率重建方法（Reference-based Two-stage Mural Image Super-Resolution Reconstruction, RTMISR）。首先，采用多尺度残差特征提取模块，通过精准捕捉高分辨率与低分辨率壁画图像间的特征联系，确保低分辨率图像信息的完整保留，并实现对壁画轮廓和部分细节的初步重建。随后，通过纹理特征增强模块，利用由粗到细的特征匹配方法，从参考图像中提取高质量纹理信息，并将其有效融合至重建图像中，以增强纹理细节表现。此外，为了确保参考图像的相关性和质量，本文设计了一种参考图像筛选模块，用以选择与目标低分辨率图像高度相关的参考图像。在壁画数据集上的实验结果表明，与SRGAN、MADNet、ESRT等代表性超分辨率方法相比，RTMISR在客观指标 PSNR、SSIM取得了更好的表现：在×2尺寸下，PSNR平均提升了2.83dB，SSIM平均提升了0.04。在×4尺寸下，PSNR平均提升了2.00dB，SSIM平均提升了0.02；在主观视觉效果上，RTMISR能够在保留壁画原始信息的同时，增强壁画图像的纹理细节，更好地平衡了模型复杂度与重建性能。