Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks

For a deeper understanding of the functional behavior of energy materials, it is necessary to investigate their microstructure via imaging techniques like scanning electron microscopy (SEM). However, active materials are often heterogeneous, necessitating quantification of features over large volumes to achieve representativity, which often requires reduced resolution for large fields of view. Cracks within Li-ion electrode particles are an example of fine features whose representative quantification requires large volumes. To overcome the trade-off between imaged volume and resolution, we deploy super-resolution generative adversarial networks (SRGANs) to super-resolve SEM images of cracked cathode materials. A quantitative analysis indicates that SRGANs outperform other networks for crack detection within aged cathode particles. This makes GANs viable for performing super-resolution on microscopy images to mitigate the trade-off between resolution and field of view, enabling representative quantification of fine features.