A super-resolution reconstruction algorithm for mural images based on improved generative adversarial network

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 proposed. This method reconstructed the detail texture of mural image better. Firstly, 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 behind. Secondly, 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 network. Furthermore, 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 details. Finally, 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 interference. The 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.