Super-Resolution Structured-Illumination X-Ray Microscopy based on Fourier Decomposition

Abstract

X-ray microscopy has become an important tool for non-destructive testing, e.g., in battery research. However, imaging a cm-scale battery cell at the desired (sub-)micrometer resolution has been challenging. State-of-the-art X-ray microscopy techniques with a suited field-of-view provide (sub-) $10\,μm$ resolution, typically limited by the detector point-spread function and the (effective) detector pixel size. This work presents a super-resolution X-ray microscopy approach overcoming both limitations. It requires a structured X-ray illumination to encode high-frequency sample information that is natively unresolved within the resolved region of support. A mathematical framework is developed that decodes this information and generates a super-resolved image from multiple acquisitions with different phase shifts of the structured X-ray illumination. The presence of this encoded high-frequency information is first experimentally demonstrated, followed by quantification and validation using a resolution test chart. A resolution improvement by a factor of 2.2 is shown. Finally, we extend the proposed super-resolution technique to X-ray microtomography. Since the image acquisition scheme is inherently multimodal, phase-contrast and dark-field X-ray images can be computed additionally. These results showcase the direct impact of the proposed technique across both non-destructive testing and biomedical imaging, alleviating pixel-size limitations in detectors and sample-size restrictions.