Multi-stage Mural Image Restoration Based on RDE-GAN Algorithm
RAN Yaqin
ZHANG Qian
Abstract：Multi-stage mural image restoration based on RDE-GAN algorithm(RDE-GAN)was proposed to address the limitations of existing methods for repairing mural images,such as missing texture details and unsuitable input pixel sizes.The entire network adopted an encoder-decoder architecture to ensure a sufficiently large receptive field for the utilization of image feature information.Firstly,a global perception network was utilized to obtain rough initial results.Secondly,a dense residual local transition network with a small receptive field was introduced.Finally,an efficient refinement network was employed to enhance the structural information of the image and the coherence of image semantics.The proposed algorithm was compared qualitatively and quantitatively with other relevant algorithms.The experimental results showed that,at[50%,60%)mask ratio,the peak signal-to-noise ratio(PSNR)of the RDE-GAN algorithm was 32.5655 dB,the structural similarity index measure(SSIM)was 0.9690,the learned perceptual image patch similarity(LPIPS)was 0.0141,and the Fréchet inception distance(FID)between generated and real images was 11.3027.Additionally,the RDE-GAN algorithm outperformed the compared algorithms in other five mask ratios.This research results can be used for the protection of cultural heritage such as murals.
Keywords：deep learningmural imagesencoder-decoderlocal dense residual moduleimage restoration
Publication Date：2024-06-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 219-225 )