A generative adversarial network model fused with a self‐attention mechanism for the super‐resolution reconstruction of ancient murals

Abstract

Abstract The problem that the texture details of low‐resolution (LR) digital images of ancient murals are ambiguous persists. To solve this problem, this study proposes a super‐resolution (SR) reconstruction method for fuzzy murals based on a generative adversarial network with self‐attention (SA). The network uses a blur kernel and realistic noise data to add blur and noise, respectively, to a high‐resolution (HR) image to obtain an original LR image. Then, a feature image with the same size as that of the input image is obtained through a SA module. Finally, the feature information extracted from the image is input into the high‐resolution image space by using a subpixel convolution layer to realize the image enlargement process from an LR to an HR. Experiments evaluate the proposed approach both objectively and subjectively. The objective evaluation results show that compared with other SR reconstruction algorithms, the proposed algorithm's peak signal‐to‐noise ratio (PSNR) is increased by 0.04 to 3.78 dB on average, and its structural similarity is increased by 0.002 to 0.191. A subjective perception evaluation shows that the developed algorithm can better reconstruct the texture details of murals, thus better meeting the visual perception needs of the public. The method proposed in this study can satisfactorily reconstruct the texture details of murals, which may provide technical guidance for the development of mural protection plans. Furthermore, it may be of certain practical significance for the SR reconstruction of ancient murals.

Keywords

• image processing
• image reconstruction
• image representation
• image sampling