Methodology and Insights for System Calibration in Multi-Angle Illumination Imaging

Multi-angle illumination-based computational microscopes have emerged as a promising class of imaging systems due to their capabilities and robustness across a wide range of applications, from biological imaging to materials inspection. In particular, quantitative phase imaging methods such as Fourier Ptychography Microscopy, Angular Ptychographic Imaging with Closed-form solutions and Kramers-Kronig relations leverage multi-angle illumination to surpass traditional space-bandwidth limitations and digitally correct aberrations. However, the performance of these systems is highly sensitive to misalignment in the illumination angles, and even minor perturbations can significantly degrade reconstruction quality and necessitate time-consuming recalibration. Thus, there is a pressing need for efficient and robust illumination angle calibration in such imaging modalities. We investigate how angular misalignments affect reconstruction fidelity and systematically evaluate a range of digital calibration strategies, including classical geometric models, cross-correlation-based methods, and learning-based approaches. These methods are benchmarked across varying signal levels and sample types. Our findings offer practical insights into selecting and deploying robust calibration techniques, ultimately supporting more resilient, reproducible, and high-throughput computational microscopy systems.