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

TL;DR

This paper introduces a pixel super-resolution method for time-stretch imaging that achieves high-resolution images at significantly lower sampling rates by exploiting inherent subpixel shifts, eliminating the need for additional hardware.

Contribution

It presents a novel pixel-SR technique that leverages natural subpixel shifts in asynchronous sampling to enhance resolution without extra hardware or active control.

Findings

Restores high-resolution images at 2-5 GSa/s, over four times lower than traditional rates.

Effective for cellular classification and biomedical diagnostics.

Cost-effective and suitable for large-scale applications.

Abstract

Based on image encoding in a serial-temporal format, optical time-stretch imaging entails a stringent requirement of state-of-the- art fast data acquisition unit in order to preserve high image resolution at an ultrahigh frame rate --- hampering the widespread utilities of such technology. Here, we propose a pixel super-resolution (pixel-SR) technique tailored for time-stretch imaging that preserves pixel resolution at a relaxed sampling rate. It harnesses the subpixel shifts between image frames inherently introduced by asynchronous digital sampling of the continuous time-stretch imaging process. Precise pixel registration is thus accomplished without any active opto-mechanical subpixel-shift control or other additional hardware. Here, we present the experimental pixel-SR image reconstruction pipeline that restores high-resolution time-stretch images of microparticles and biological cells (phytoplankton) at a relaxed sampling rate (approx. 2--5 GSa/s) --- more than four times lower than the originally required readout rate (20 GSa/s) --- is thus effective for high-throughput label-free, morphology-based cellular classification down to single-cell precision. Upon integration with the high-throughput image processing technology, this pixel-SR time- stretch imaging technique represents a cost-effective and practical solution for large scale cell-based phenotypic screening in biomedical diagnosis and machine vision for quality control in manufacturing.