An improved algorithm for superresolution reconstruction of ancient murals with a generative adversarial network based on asymmetric pyramid modules

Abstract

Abstract Ancient Chinese murals are true portrayals of ancient Chinese life, but well-preserved murals are rare. Therefore, ancient mural preservation and repair are critical. To address the poor superresolution reconstruction of mural images with unclear textures and fuzzy details, we developed an improved generative adversarial network (GAN) algorithm based on asymmetric pyramid modules for ancient mural superresolution reconstruction. Asymmetric pyramid modules, which are composed of a series of dense compression units, were used to learn image features. To analyze the reconstructed image features, a perceptual loss function was integrated to optimize the model performance. The use of the improved algorithm for low-resolution mural images increased the image resolution while preserving their original feature details and textures, and the improvement effect was visually observed in terms of indices such as the peak signal-to-noise ratio and structural similarity. Compared with other superresolution-related algorithms, the proposed model increased the peak signal-to-noise ratio by 0.20–6.66 dB. The GAN-based mural superresolution reconstruction algorithm proposed in this study effectively improved the performance of reconstructed high-resolution mural images, which increases the significance of the reconstructed image for future research.

Keywords

• Image processing
• Superresolution reconstruction
• Generative adversarial network
• Deep learning