As an indispensable tool for observing the microworld, optical microscopy has promoted the development of various fields, including biology, medicine, physics, and materials. However, optical diffraction imposes limitations on spatial resolution on optical microscopy, which hinders exploration of fine structures.
To overcome the limitations of resolution, several super-resolution microscopy techniques have been proposed based on diverse principles. However, these techniques usually gain superior resolution at the expense of low imaging speed, so achieving high-resolution ultrafast imaging that can detect fast dynamics with fine structures remains a major challenge.
Recently, a research team from East China Normal University, Shenzhen University, and Peking University resolved the discrepancy between spatial resolution and imaging speed. As stated in advanced photonics, they have achieved superior high-speed resolution by developing an effective technique called high-resolution time-compression microscopy (TCSRM). TCSRM integrates enhanced temporal compact microscopy with deep learning-based super-resolution image reconstruction. Enhanced temporal compression microscopy improves imaging speed by reconstructing multiple images from a single compressed image, and image reconstruction based on deep learning achieves the effect of super resolution without reducing imaging speed. Their iterative image reconstruction algorithm contains motion estimation, merging estimation, scene correction, and super-resolution processing to extract super-resolution image sequences from compressed and reference measurements.
Their studies demonstrated the high-resolution imaging capability of TCSRM both in theory and experiment. To demonstrate the imaging capability of TCSRM, they imaged fluorescent beads streaming in a nanochannel, achieving an impressive frame rate of 1,200 fps and a spatial resolution of 100 nm.
According to corresponding author Xian Zhang, professor and deputy director of the State Key Laboratory for Microspectroscopy at East China Normal University, “This work provides a powerful tool for monitoring the high-speed dynamics of microstructures, especially in hydromechanics and biomedical fields, such as microflow velocity measurement, and organelle interactions.” , intracellular transmission processes, and neuronal dynamics.” Zhang adds, “The TCSRM framework can also provide guidelines for achieving higher imaging speed and spatial resolution in stereolithography, coherent diffraction imaging, and measurement of marginal projection features.”
