Compressed Sensing STORM Super-Resolution Image Reconstruction Based on Noise Correction-Principal Component Analysis Preprocessing Algorithm
【Abstract】The low temporal resolution of stochastic optical reconstruction microscopy (STORM) limits its ability to observe the dynamic events in live cells. Further, the post-processing analysis and reconstruction algorithms have an important effect on super-resolution images. In this study, we report a new noise-correction principal component analysis method for single-molecule localization microscopy against fluorescent spot overlapping and excessive background noise in a single frame of images owing to high-density labeling and high camera-sampling frequency. The proposed method can improve the positioning accuracy of existing localization methods by preprocessing the raw images acquired by the single molecule localization microscopy before reconstruction. In addition, this method can accurately distinguish the overlapping molecules. Therefore, it is suitable for samples exhibiting a high fluorophore density. Thus, the proposed method improves the temporal resolution of super-resolution imaging, providing a powerful technical support for the STORM imaging of live cells.
【Keywords】 biotechnology; stochastic optical reconstruction microscopy; principal component analysis; denoising algorithm; super-resolution optical imaging;
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