| Abstract: |
| Super-resolution fluorescence microscopy enables nanoscale visualization of subcellular structures, high-performance reconstruction algorithms are imperative for further improving imaging quality. To improve the spatial resolution of fluorescence microscopy images, we present a deconvolution algorithm GEOREC, which is a robust and efficient super-resolution reconstruction framework. A background term is incorporated to achieve dynamic and precise removal of background fluorescence. We further integrate the geometric prior derived from shape operator in the model to preserve the sharp edges of the structure during denoising and background removal. In addition, we adopt a coarse-to-fine grid optimization strategy to significantly reduce computation time. Comprehensive evaluations on simulated data and real experimental images (microtubules, endoplasmic reticulum, Escherichia coli, etc.) demonstrate that GEOREC achieves high-fidelity and efficient super-resolution reconstruction and outperforms the state-of-the-art deconvolution methods. |
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