Abstract
Capturing biological dynamics with high spatiotemporal resolution demands the advancement in imaging technologies. Super-resolution fluorescence microscopy offers spatial resolution surpassing the diffraction limit to resolve near-molecular-level details. While various strategies have been reported to improve the temporal resolution of super-resolution imaging, all super-resolution techniques are still fundamentally limited by the trade-off associated with the longer image acquisition time that is needed to achieve higher spatial information. Here, we demonstrated an example-based, computational method that aims to obtain super-resolution images using conventional imaging without increasing the imaging time. With a low-resolution image input, the method provides an estimate of its super-resolution image based on an example database that contains super- and low-resolution image pairs of biological structures of interest. The computational imaging of cellular microtubules agrees approximately with the experimental super-resolution STORM results. This new approach may offer potential improvements in temporal resolution for experimental super-resolution fluorescence microscopy and provide a new path for large-data aided biomedical imaging.
Highlights
Super-resolution fluorescence imaging techniques have overcome the optical diffraction limit of conventional fluorescence microscopy, allowing visualization of biological structures with near-molecular-scale resolution[1,2]
Image is segmented into patches. (b) For a representative patch (i) of the input LR image, its LR matches in the database are selected based on the evidence potential ψi(xi), along with their corresponding SR image counterparts. (c) The selection of the best SR candidates considers the overlapping potential θij and θik between neighboring SR candidate patches
These example images of natural scenes are typically unrelated to the LR image of interest, and the resolution improvement relies on the statistical restoration of the missing high-frequency components in the LR image by inference from the features of the frequency compositions in the examples[18]
Summary
Capturing biological dynamics with high spatiotemporal resolution demands the advancement in imaging technologies. We anticipate this approach to work better for those structures that prior knowledge of their shapes exists, which would facilitate the construction of the example library We expect this computation-based method to improve image resolution from conventional LR images to be useful for imaging fast dynamics of known biological structures (e.g. microtubule dynamics) with higher resolution. More advanced algorithms will be developed to sort and process the on-line data, construct such libraries, and perform SR image reconstruction With further advancement, this example-based approach may enable computational super-resolution imaging of many biological structures and provide a new path for large-imaging-data aided biomedical research
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