Enhance! Deep learning tool boosts X-ray imaging resolution and hydrogen fuel cell performance

By NEIL MARTIN, UNSW Newsroom – 15/02/2023

UNSW Sydney researchers develop algorithm to significantly enhance images of hydrogen fuel cells, with potential future application in medical scanning.

Researchers from UNSW Sydney have developed an algorithm which produces high-resolution modelled images from lower-resolution micro X-ray computerised tomography (CT).
The new process, detailed in a paper published in Nature Communications, has been tested on individual hydrogen fuel cells to accurately model the interior in precise detail and potentially improve the efficiency of them.

But the researchers say it could also be used in future on human X-rays to give medical professionals a better understanding of tiny cellular structures inside the body, which could allow for better and faster diagnosis of a wide range of diseases.
The team, featuring 

Professor Ryan Armstrong,  Professor Peyman MostaghimiDr Ying Da Wang, and Kunning Tang from the School of Mineral and Energy Resources Engineering and Prof Chuan Zhao and Dr Quentin Meyer from the School of Chemistry, developed the algorithm to improve the understanding of what is happening inside a Proton Exchange Membrane Fuel Cell (PEMFC).

PEMFCs use hydrogen fuel to generate electricity and are a quiet, and clean energy source that can power homes, vehicles, and industries.