Light induced fluorescent microscopy has long been developed to observe and understand the object at microscale, such as cellular sample. However, the transfer function of lense-based imaging system limits the resolution so that the fine and detailed structure of sample cannot be identified clearly. The techniques of resolution enhancement are fascinated to break the limit of resolution for objective given. In the past decades, the resolution enhancement imaging has been investigated through variety of strategies, including photoactivated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), stimulated emission depletion (STED), and structure illuminated microscopy (SIM). In those methods, only SIM can intrinsically improve the resolution limit for a system without taking the structure properties of object into account. In this paper, we develop a SIM associated with Bayesian estimation, furthermore, with optical sectioning capability rendered from HiLo processing, resulting the high resolution through 3D volume. This 3D SIM can provide the optical sectioning and resolution enhancement performance, and be robust to noise owing to the Data driven Bayesian estimation reconstruction proposed. For validating the 3D SIM, we show our simulation result of algorithm, and the experimental result demonstrating the 3D resolution enhancement.
Hsi-Hsun Chen, Yuan Luo, and Vijay R. Singh, "Hybrid-coded 3D structured illumination imaging with Bayesian estimation
(Conference Presentation)," Proc. SPIE 9713, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXIII, 97131P (Presented at SPIE BiOS: February 17, 2016; Published: 27 April 2016); https://doi.org/10.1117/12.2212589.4848767294001.
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