Various super-resolution microscopy techniques have been presented to explore fine structures of biological specimens. However, the super-resolution capability is often achieved at the expense of reducing imaging speed by either point scanning or multiframe computation. The contradiction between spatial resolution and imaging speed seriously hampers the observation of high-speed dynamics of fine structures. To overcome this contradiction, here we propose and demonstrate a temporal compressive super-resolution microscopy (TCSRM) technique. This technique is to merge an enhanced temporal compressive microscopy and a deep-learning-based super-resolution image reconstruction, where the enhanced temporal compressive microscopy is utilized to improve the imaging speed, and the deep-learning-based super-resolution image reconstruction is used to realize the resolution enhancement. The high-speed super-resolution imaging ability of TCSRM with a frame rate of 1200 frames per second (fps) and spatial resolution of 100 nm is experimentally demonstrated by capturing the flowing fluorescent beads in microfluidic chip. Given the outstanding imaging performance with high-speed super-resolution, TCSRM provides a desired tool for the studies of high-speed dynamical behaviors in fine structures, especially in the biomedical field.
Compressed ultrafast photography (CUP) is the fastest receive-only single-shot imaging technique up to now. By combining compressed sensing and streak imaging, CUP is able to capture ultrafast dynamics in a single shot. As a powerful tool for researching ultrafast phenomena, it has been widely applied in lots of areas. To meet the demand for more precise dynamics information and higher dimension in some application, many improvements have been conduct in CUP. For example, we have raised total variation-block match 3D filter algorithm and augmented Lagrange-deep learning hybrid algorithm to improve the reconstructed image quality of CUP, and set up a stereo-volumetric CUP system to capture 5 dimension dynamic information in a single shot. Besides, we have also developed another single-shot ultrafast optical imaging technique, chirped spectral mapping ultrafast photography (CSMUP), which utilized the spectral-temporal mapping to exact temporal information from hyperspectral image.
In ultrafast optical imaging, it is critical to obtain the spatial structure, temporal evolution, and spectral composition of the object with snapshots in order to better observe and understand unrepeatable or irreversible dynamic scenes. However, so far, there are no ultrafast optical imaging techniques that can simultaneously capture the spatial–temporal–spectral five-dimensional (5D) information of dynamic scenes. To break the limitation of the existing techniques in imaging dimensions, we develop a spectral-volumetric compressed ultrafast photography (SV-CUP) technique. In our SV-CUP, the spatial resolutions in the x, y and z directions are, respectively, 0.39, 0.35, and 3 mm with an 8.8 mm × 6.3 mm field of view, the temporal frame interval is 2 ps, and the spectral frame interval is 1.72 nm. To demonstrate the excellent performance of our SV-CUP in spatial–temporal–spectral 5D imaging, we successfully measure the spectrally resolved photoluminescent dynamics of a 3D mannequin coated with CdSe quantum dots. Our SV-CUP brings unprecedented detection capabilities to dynamic scenes, which has important application prospects in fundamental research and applied science.
Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields.
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