Multi-frame blind deconvolution using X-ray microscope images of an in-plane rotating sample

We propose a multi-frame blind deconvolution method using an in-plane rotating sample optimized for X-ray microscopy, where the application of existing deconvolution methods is technically difficult. Untrained neural networks are employed as the reconstruction algorithm to enable robust reconstruction against stage motion errors caused by the in-plane rotation of samples. From demonstration experiments using full-field X-ray microscopy with advanced Kirkpatrick-Baez mirror optics at SPring-8, a spatial resolution of 34 nm (half period) was successfully achieved by removing the wavefront aberration and improving the apparent numerical aperture. This method can contribute to the cost-effective improvement of X-ray microscopes with imperfect lenses as well as the reconstruction of the phase information of samples and lenses.