Structured illumination microscopy (SIM) has been widely applied in the superresolution imaging of subcellular dynamics in live cells. Higher spatial resolution is expected for the observation of finer structures. However, further increasing spatial resolution in SIM under the condition of strong background and noise levels remains challenging. Here, we report a method to achieve deep resolution enhancement of SIM by combining an untrained neural network with an alternating direction method of multipliers (ADMM) framework, i.e., ADMM-DRE-SIM. By exploiting the implicit image priors in the neural network and the Hessian prior in the ADMM framework associated with the optical transfer model of SIM, ADMM-DRE-SIM can further realize the spatial frequency extension without the requirement of training datasets. Moreover, an image degradation model containing the convolution with equivalent point spread function of SIM and additional background map is utilized to suppress the strong background while keeping the structure fidelity. Experimental results by imaging tubulins and actins show that ADMM-DRE-SIM can obtain the resolution enhancement by a factor of ∼1.6 compared to conventional SIM, evidencing the promising applications of ADMM-DRE-SIM in superresolution biomedical imaging.
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.
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