Bi-Dimensional Large-Kernel Attention Network for Digital Core Images

Digital rock techniques are increasingly important in petroleum exploration and petrophysics. Digital rocks are typically acquired via scanning or imaging techniques, but the resulting images may lack clear, detailed information due to resolution limitations. Super-resolution reconstruction using deep learning offers new possibilities for digital rock technology development. In current research on super-resolution reconstruction of digital rock images, most networks employ attentional mechanisms in a single dimension, ignoring more comprehensive interactions from both spatial and channel dimensions.

To address the above problems, we propose a bi-dimensional large kernel attention network for super-resolution reconstruction of digital rock images. The network consists of three components: a bi-dimensional large kernel building block, a contrast channel attention block and an enhanced spatial attention block. In addition, the traditional method of stacking network modules to build the network leads to an increase in computation and network size, so we adopt Transformer's MetaFormer architecture, which integrates multivariate feature extraction to improve the efficiency of the network. In the process of feature information circulation, we effectively prevent shallow feature loss by two efficient attention modules working at different network depth positions. Extensive experiments on Sandstone2D and Carbonate2D rock datasets show that our proposed model significantly outperforms existing image super-resolution networks.