All-passive pixel super-resolution of time-stretch imaging

Area of Science:

• Optics and Photonics
• Biomedical Imaging
• Computational Imaging

Background:

• Optical time-stretch imaging offers high frame rates but requires fast data acquisition, limiting its practical use.
• Preserving high image resolution at ultrahigh frame rates is a significant challenge in current time-stretch imaging systems.

Purpose of the Study:

• To introduce a pixel super-resolution (pixel-SR) technique specifically designed for time-stretch imaging.
• To enable high-resolution imaging at a relaxed sampling rate, overcoming hardware limitations.

Main Methods:

• Utilizing inherent subpixel shifts from asynchronous digital sampling in continuous time-stretch imaging.
• Implementing a pixel-SR image reconstruction pipeline without additional hardware.
• Reconstructing high-resolution images from data acquired at a relaxed sampling rate (2-5 GSa/s).

Main Results:

• Successfully restored high-resolution time-stretch images of microparticles and phytoplankton.
• Achieved image reconstruction at sampling rates over four times lower than previously required (20 GSa/s).
• Demonstrated the effectiveness for high-throughput, label-free, morphology-based cellular classification.

Conclusions:

• The pixel-SR technique effectively preserves resolution in time-stretch imaging at reduced sampling rates.
• This approach offers a cost-effective and practical solution for large-scale cell-based phenotypic screening.
• The technology has potential applications in biomedical diagnosis and machine vision for manufacturing quality control.