Application Of Deep Learning Model With Attention Mechanism In Super Resolution Reconstruction Of Ancient Murals

The ancient murals are full of colorful contents and diverse scenes.They combine religious culture and art organically with unique artistic techniques,presenting a distinctive and unique aesthetic feeling.In the years of sculpture,sand erosion and man-made destruction,they are scarred.The realization of high-resolution mural restoration through digital image processing technology is a modern technology of protecting,repairing and inheriting culture with high technology as the core.It provides new ideas for inheriting 5000 years of Chinese culture,improving the scientific and technological support level of mural protection in China,and providing new ideas for the digital preservation of murals in China.This paper takes ancient murals as the research object,studies the phenomena of texture loss and ambiguity of murals,and proposes a deep learning model integrating attention mechanism in the super-resolution reconstruction method of ancient murals,which optimizes the reconstruction of murals,so that mural images have better effects in high-frequency detail information and image perception quality,and opens a new channel for the digital protection of murals.First of all,aiming at the problem of blurred texture details of murals,based on the generative adversarial network,the self-attention module is added to the reconstruction module of the generation network,and the high-level features of a high-resolution image are generated according to the information of all pixels in the low-resolution image,which greatly strengthens the feature extraction ability and realizes the process of image from low-resolution to high-resolution amplification.Then,in order to solve the problems of poor perception quality of mural images and high network training cost,the network structure is optimized with the help of convolutional block attention module and skip connection in the framework of zero sample super-resolution reconstruction algorithm,and the specific model is obtained by training the images themselves.Compared with the traditional super-resolution reconstruction algorithm,the efficiency of the algorithm proposed in this paper is verified,and a better reconstruction effect is obtained.Finally,based on the proposed algorithm of mural super-resolution reconstruction,an online super-resolution reconstruction system is designed and implemented.The mural online super-resolution reconstruction system designed and implemented in this paper,on the one hand,realizes the visualization operation and local saving of mural images for online super-resolution reconstruction,on the other hand,realizes the management and permission setting of users,which has certain auxiliary value for practitioners in the field of mural repair.