Abstract
High-quality digital rock images are essential for subsequent
high-precision numerical simulations. But limited by the imaging
capability of computed tomography (CT), high resolution digital rock
images with wide imaging field of view (FOV) cannot be acquired
simultaneously. To cope with this constraint, we propose a novel Multi
Attention Super-Resolution Neural Network (MASR) that enhances the
resolution of images with wide FOV. Considering that textures and edges
are more crucial in digital rocks, MASR introduces the component
attention mechanism of Component Divide-and-Conquer Super-Resolution
(CDCSR) model. By redesigning the hourglass network with spatial and
channel attention mechanisms, proposing a spatial attention-based mask
module, and optimizing the component attention mask calculation process,
MASR delivers higher information utilization with fewer parameters and
faster training than CDCSR. And we optimize the depth of MASR to trade
off speed and super-resolution quality. Furthermore, we retrained
several state-of-the-art models. Through quantitative evaluations and
qualitative visualizations, it is verified that MASR can recover sharper
edges while removing noise, and obtain digital rock images with superior
quality and reliability. The pixelwise relative errors of MASR
reconstructions are reduced by 15% to 26% over bicubic interpolation
method. Our codes are publicly available at
https://github.com/MHDXing/MASR-for-Digital-Rock-Images.