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Single‐molecule localization microscopy (SMLM), which has revolutionized nanoscale imaging, faces challenges in densely labelled samples due to fluorophore clustering, leading to compromised localization accuracy. In this paper, we propose a novel convolutional neural network (CNN)‐assisted approach to address the issue of locating the clustered fluorophores. Our CNN is trained on a diverse data set of simulated SMLM images where it learns to predict point spread function (PSF) locations by generating Gaussian blobs as output. Through rigorous evaluation, we demonstrate significant improvements in PSF localization accuracy, especially in densely labelled samples where traditional methods struggle. In addition, we employ blob detection as a post‐processing technique to refine the predicted PSF locations and enhance localization precision. Our study underscores the efficacy of CNN in addressing clustering challenges in SMLM, thereby advancing spatial resolution and enabling deeper insights into complex biological structures. [ABSTRACT FROM AUTHOR]

Super-resolution imaging techniques have fundamentally changed our understanding of cellular architecture and dynamics by surpassing the diffraction limit and enabling the visualization of subcellular details. The popular super-resolution method known as stochastic optical reconstruction microscopy (STORM) relies on the exact localization of single fluorescent molecules. The significance of employing Vectashield as a mounting medium for the super-resolution imaging scheme called direct STORM has recently been explored. Alexa Fluor 647 (AF647), one of the most popular dyes, shows significant blinking in Vectashield. However, to observe prominent blinking of the fluorophore for the reconstruction of super-resolved images, the power of the excitation laser needs to be tuned. This work demonstrates the tuning of excitation power density in the sample plane for superior imaging performance using AF647 in Vectashield. Samples comprising MDA-MB-231 breast cancer cell line are used for the experiments. The actin filaments of the cell are stained with phalloidin-conjugated AF647 dye. For the experiment, we employ a low-cost openFrame-based STORM system equipped with a programmable Arduino-regulated laser source emitting at 638 nm. An excitation power density of 0.60 kW/cm2 at 638 nm in the sample plane is observed to maximize the signal-to-noise ratio, the number of switching events, and the number of photons detected per event during image acquisition, thereby leading to the best imaging performance in terms of resolution. The outcome of this work will promote further STORM-based super-resolved imaging applications in cell biology using Alexa Fluor 647 in Vectashield. [ABSTRACT FROM AUTHOR]

In this paper we propose a zonal wavefront sensing scheme that facilitates wavefront measurement with enhanced sensitivity at the standard video rate. We achieve this enhanced sensitivity by implementing a sequential display of binary holograms described over each zone sampling the incident wavefront with the help of a ferroelectric liquid crystal spatial light modulator. By keeping the number of active zones as 24 and using a camera with an imaging frame rate equal to the binary hologram display rate of the spatial light modulator, we are able to reach a sensing frame rate of 60 Hz. In addition to enhanced sensitivity, the proposed scheme facilitates zone-wise tuning of binary holograms and eliminates the possibility of any crosstalk between adjacent zones. We perform a proof-of-principle experiment that validates the proposed zonal wavefront sensing scheme and demonstrates its advantages. [ABSTRACT FROM AUTHOR]