关键词:
mural image
super-resolution reconstruction
generative adversarial network
information distillation block(IDB)
feature fusion
摘要:
In order to solve the problem of the lack of ornamental value and research value of ancient mural paintings due to low resolution and fuzzy texture details,a super resolution(SR)method based on generative adduction network(GAN)was *** method reconstructed the detail texture of mural image ***,in view of the insufficient utilization of shallow image features,information distillation blocks(IDB)were introduced to extract shallow image features and enhance the output results of the network ***,residual dense blocks with residual scaling and feature fusion(RRDB-Fs)were used to extract deep image features,which removed the BN layer in the residual block that affected the quality of image generation,and improved the training speed of the ***,local feature fusion and global feature fusion were applied in the generation network,and the features of different levels were merged together adaptively,so that the reconstructed image contained rich ***,in calculating the perceptual loss,the brightness consistency between the reconstructed fresco and the original fresco was enhanced by using the features before activation,while avoiding artificial *** experimental results showed that the peak signal-to-noise ratio and structural similarity metrics were improved compared with other algorithms,with an improvement of 0.512 dB-3.016 dB in peak signal-to-noise ratio and 0.009-0.089 in structural similarity,and the proposed method had better visual effects.