Deep Learning-Enabled Pixel-Super-Resolved Quantitative Phase Microscopy from Single-Shot Aliased Intensity Measurement

A new technique of deep learning-based pixel-super-resolved quantitative phase microscopy (DL-SRQPI) is proposed, achieving rapid wide-field high-resolution and high-throughput quantitative phase imaging (QPI) from single-shot low-resolution intensity measurement. By training a neural network with sufficiently paired low-resolution intensity and high-resolution phase data, the network is empowered with the capability to robustly reconstruct high-quality phase information from a single frame of an aliased intensity image. As a graphics processing units-accelerated computational method with minimal data requirement, DL-SRQPI is well-suited for live-cell imaging and accomplishes high-throughput long-term dynamic phase reconstruction. The effectiveness and feasibility of DL-SRQPI have been significantly demonstrated by comparing it with other traditional and learning-based phase retrieval methods. The proposed method has been successfully implemented into the quantitative phase reconstruction of biological samples under bright-field microscopes, overcoming pixel aliasing and improving the spatial-bandwidth product significantly. The generalization ability of DL-SRQPI is illustrated by phase reconstruction of Henrietta Lacks cells at various defocus distances and illumination patterns, and its high-throughput anti-aliased phase imaging performance is further experimentally validated. Given its capability of achieving pixel super-resolved QPI from single-shot intensity measurement over conventional bright-field microscope hardware, the proposed approach is expected to be widely adopted in life science and biomedical workflows. A deep learning-based technique for quantitative phase microscopy with pixel super-resolution capability is proposed, which enables full-field-of-view, high-resolution, and high-speed phase imaging of unlabeled biological specimens with only a single frame of a low-resolution intensity image as input, demonstrating promising applications in high-throughput cellular dynamics analysis.image