Computational Reconstruction for Lensless Structured Illumination Microscopy

Lensless structured illumination microscopy offers a promising route to overcome the space-bandwidth limitations of conventional objective-based systems, enabling large fields of view and enhanced resolution in a compact architecture. By combining tailored illumination patterns with lens-free detection and computational reconstruction, such systems are well suited for high-throughput fluorescence imaging across diverse assay formats, including microfluidic chips and multi-well plates. At the same time, the resulting inverse problem couples illumination, propagation, sensor sampling, and noise, calling for advanced reconstruction strategies that balance image quality, robustness, and computational efficiency.

This internship will focus on developing and benchmarking reconstruction algorithms for lensless structured illumination fluorescence imaging. The work will center on the computational pipeline that transforms multiplexed raw measurements into quantitatively reliable images, exploring a spectrum of approaches from physics-based inverse methods to data-driven schemes using modern machine learning. Depending on the student’s background, tasks may include forward modelling, algorithm and network development, dataset generation, and the definition of application-oriented image quality metrics for high-throughput biological measurements.