![]() ![]() In other words, the time-stretch image is easily affected by aliasing if sampled at a lower rate. the effective spatial pixel size, is the limiting factor of the spatial resolution in time-stretch imaging, especially in the regime of high analog bandwidth (beyond 1 GHz). As a consequence, it is common that the sampling rate of the digitizer, i.e. This technical constraint of GVD explains that the overall space-to-time conversion achieved in time-stretch imaging is generally limited to few tens of picoseconds (or less) per resolvable image point. Even worse, achieving high GVD-to-loss ratio becomes increasingly difficult as the operation wavelengths move from the telecommunication band to the shorter-wavelength window, which is favourable for biomedical applications, not to mention the benefit of higher diffraction-limited resolution at the shorter wavelengths. To combat against the nonlinear signal distortion and amplifier noise, it also necessitates careful designs of multiple and cascaded amplifiers that complicate the system architecture. Although optical amplification can mitigate the dispersive loss, progressively higher amplifier gain results in excessive amplifier noise, which in turn degrades the SNR. However, as governed by the Kramers-Kronig relations, high GVD comes at the expense of high optical attenuation that deteriorates the signal-to-noise ratio (SNR) of the images 8. To avoid using these state-of-the-art digitizers, which incur prohibitively high cost, the common strategy is to further stretch the spectrally-encoded waveform with an even higher GVD such that the encoded image can be resolved by the cost-effective, lower-bandwidth digitizers. Second, time-stretch imaging inevitably requires the electronic digitizer with an ultrahigh sampling rate (≥40 GSa/s) in order to resolve the time-stretched waveform. First, sufficiently high GVD in a dispersive medium (≈1 ns nm −1 at the wavelengths of 1–1.5 μm) is needed to ensure the time-stretched waveform to be the replica of the image-encoded spectrum. In order to guarantee high spatial resolution that is ultimately determined by the diffraction limit, two interrelated features have to be considered. This constraint stems from its image encoding principle that relies on real-time wavelength-to-time conversion of spectrally-encoded waveform, through group velocity dispersion (GVD), to capture image with a single-pixel photodetector. Nevertheless, a key challenge of time-stretch imaging limiting its widespread utility is that the spatial resolution is very often compromised at the ultrafast imaging rate. This combined feature makes it unique for ultrahigh-throughput monitoring and screening applications, ranging from barcode recognition and web-inspection in industrial manufacturing 6 to imaging cytometry in life sciences and clinical diagnosis 7. ![]() Among all techniques, optical time-stretch imaging not only can achieve an ultrafast imaging rate of MHz-GHz, but also allow continuous operation in real time. High-speed optical imaging with the temporal resolution reaching the nanosecond or even picosecond regime is a potent tool to unravel ultrafast dynamical processes studied in a wide range of disciplines 1, 2, 3, 4, 5. 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. 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 (≈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. Precise pixel registration is thus accomplished without any active opto-mechanical subpixel-shift control or other additional hardware. ![]() It harnesses the subpixel shifts between image frames inherently introduced by asynchronous digital sampling of the continuous time-stretch imaging process. Here, we propose a pixel super-resolution (pixel-SR) technique tailored for time-stretch imaging that preserves pixel resolution at a relaxed sampling rate. 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. ![]()
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