Virtual restoration of ancient mold-damaged paintings based on spectral-guided asymmetric autoencoder for hyperspectral images

Ancient Chinese silk paintings represent a remarkable fusion of art and culture, functioning as key artifacts with historical, present, and future significance. However, inappropriate preservation has resulted in different types of deterioration, including mold infestation, which causes pigment fading, discoloration, structural fragility, and breakdown. This paper proposed the Spectral-Guided Restoration Asymmetric Autoencoder (MoldSGR-AsyAutoencoder) for hyperspectral virtual restoration of mold-affected silk paintings. Through the mold spectral response analysis, the spectral invariant characteristics of mold spots on silk paintings in the near-infrared (NIR) were found. The similarity discrimination strategy based on spectral-spatial features was developed. An asymmetric autoencoder model with multistage feature extraction was then designed to achieve pixel-level hyperspectral virtual recovery of mold-affected regions. Experimental results demonstrate that this method achieved excellent virtual restoration in both simulated and real-mold-affected regions, providing a robust theoretical foundation and technical support for the hyperspectral virtual restoration of mold-affected regions on silk paintings.