Assessment of deep-learning-based resolution recovery algorithm relative to imaging system resolution and feature size

Abstract High-resolution X-ray microscopy is crucial for non-destructive materials characterization, but achieving both high resolution and maintaining a wide field of view often necessitates time-consuming approaches. Deep learning super-resolution methods based on convolutional neural networks are bridging this gap to obtain high-resolution usable data for analysis from low-resolution images. This study evaluates a novel deep learning-based algorithm designed to overcome traditional limitations by learning a spatially varying point spread function from a set of registered low- and high-resolution image pairs. With a systematic methodology, we evaluated the algorithm’s superior performance in recovering features across a wide range of resolutions with increasing image quality degradation. It was also benchmarked against a classical iterative Richardson-Lucy deconvolution algorithm, and a well-known deep-learning-based super-resolution convolutional neural network SRCNN algorithm for the same images. Qualitative and quantitative evaluations using simulated foam phantoms showed that our algorithm shows excellent feature recovery, within 5 % of the ground truth, even for a large resolution ratio of 7:1 between the high- and low-resolution image pairs. Multiscale investigations on real data of porous material and a semiconductor device are also presented to highlight its feature recovery performance and versatility in real-world scenarios.