Mural Image Restoration Method Based on CSWin-Transformer and Gate Convolution

Dunhuang mural are precious cultural heritage, but the existing mural paintings have a large number of broken phenomena. Aiming at the problems of high computational complexity, blurred texture and insufficient feature extraction faced by the existing image restoration methods in dealing with Dunhuang frescoes, a mural image restoration method combining CSWin-Transformer (cross stripe window-Transformer) and gate convolution is proposed. A parallel network consisting of a global-layer network and a local-layer gate convolution residual dense network is constructed to enhance the image feature extraction capability by using the stripe window and to improve the accuracy of structural texture restoration by the gate convolution residual block. The global-local feature fusion module is designed to fuse the feature images output from the global and local layers to maintain the overall consistency of the repair results. The information interaction between global and local layers is achieved by establishing a shared attention mechanism, while the spectral normalized Markov discriminant model is used for adversarial training in order to complete the restoration of broken murals. Through the restoration experiments on real broken murals, the results show that the proposed method is superior to the compared methods in terms of subjective and objective indexes. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.