The mural image virtual restoration method based on bi-generator generative adversarial network
Aiming at the existing fresco restoration methods based on generative adversarial networks,their generated samples lack diversity and are prone to large-scale feature loss and other problems.A virtual restoration method for fresco images based on bi-generator generative adversarial network(BGGAN)is proposed.Firstly,sample generation from two random directions ensures the diversity of generated samples.Secondly,for the Dilate U-Net Kares generator model,the inflated convolutional expansion rate in the downsampling stage is improved and the pooling operation is eliminated.Finally,the loss function is designed to combine the MSE loss with the adversarial loss,and the feature gradient of the generated samples is constrained by λG.Restoration tests are performed on the collected mural dataset,and the test results are compared with multiple image restoration methods.The results show that the image restoration results obtained by the proposed algorithm have clearer details.The peak signal-to-noise ratio(PSNR)of the restored image is improved by about 1.12 dB on average compared to the comparison model,and the structural similarity(SSIM)is improved by about 0.047 on average.
image processingvirtual restoration of muralsgenerative adversarial networkU-Net
NETL
NSTL
万方数据
