Digital core image reconstruction based on residual self-attention generative adversarial networks

In order to perform accurate physical analysis of digital core, the reconstruction of high-quality digital core image has become a problem to be resolved at present. In this paper, a digital core image reconstruction method based on the residual self-attention generative adversarial networks is proposed. In the process of digital core image reconstruction, the traditional generative adversarial networks (GANs) can obtain high resolution detail features only by the spatial local point generation in low resolution details, and the far away dependency can only be processed by multiple convolution operations. In view of this, in this paper the residual self-attention block is introduced in the traditional GANs, which can strengthen the correlation learning between features and extract more features. In order to analyze the quality of generated shale images, in this paper the Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) are used to evaluate the consistency of Gaussian distribution between reconstructed shale images and original ones, and the two-point covariance function is used to evaluate the structural similarity between reconstructed shale images and original ones. Plenty experiments show that the reconstructed shale images by the proposed method in the paper are closer to the original images and have better effect, compared to those of the state-of-art methods.