Improving the spatial resolution of a fluorescence microscope has been an ongoing challenge in the imaging community. To address this challenge, a variety of approaches have been taken, ranging from instrumentation development to image post-processing. An example of the latter is deconvolution, where images are numerically deblurred based on a knowledge of the microscope point spread function. However, deconvolution can easily lead to noise-amplification artifacts. Deblurring by post-processing can also lead to negativities or fail to conserve local linearity between sample and image. We describe here a simple image deblurring algorithm based on pixel reassignment that inherently avoids such artifacts and can be applied to general microscope modalities and fluorophore types. Our algorithm helps distinguish nearby fluorophores even when these are separated by distances smaller than the conventional resolution limit, helping facilitate, for example, the application of single-molecule localization microscopy in dense samples. We demonstrate the versatility and performance of our algorithm under a variety of imaging conditions.
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http://dx.doi.org/10.1101/2023.07.24.550382 | DOI Listing |
Nat Commun
November 2024
Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, Rehovot, Israel.
We present super-resolved coherent anti-Stokes Raman scattering (CARS) microscopy by implementing phase-resolved image scanning microscopy, achieving up to two-fold resolution increase as compared with a conventional CARS microscope. Phase-sensitivity is required for the standard pixel-reassignment procedure since the scattered field is coherent, thus the point-spread function is well-defined only for the field amplitude. We resolve the complex field by a simple add-on to the CARS setup enabling inline interferometry.
View Article and Find Full Text PDFAdv Photonics
October 2023
Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States.
Improving the spatial resolution of a fluorescence microscope has been an ongoing challenge in the imaging community. To address this challenge, a variety of approaches have been taken, ranging from instrumentation development to image postprocessing. An example of the latter is deconvolution, where images are numerically deblurred based on a knowledge of the microscope point spread function.
View Article and Find Full Text PDFNat Commun
May 2024
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
Fluorescence microscopy has undergone rapid advancements, offering unprecedented visualization of biological events and shedding light on the intricate mechanisms governing living organisms. However, the exploration of rapid biological dynamics still poses a significant challenge due to the limitations of current digital camera architectures and the inherent compromise between imaging speed and other capabilities. Here, we introduce sHAPR, a high-speed acquisition technique that leverages the operating principles of sCMOS cameras to capture fast cellular and subcellular processes.
View Article and Find Full Text PDFSuper-resolution microscopy has revolutionized the field of biophotonics by revealing detailed 3D biological structures. Nonetheless, the technique is still largely limited by the low throughput and hampered by increased background signals for dense or thick biological specimens. In this paper, we present a pixel-reassigned continuous line-scanning microscope for large-scale high-speed 3D super-resolution imaging, which achieves an imaging resolution of 0.
View Article and Find Full Text PDFbioRxiv
September 2023
Department of Biomedical Engineering, Boston University, MA 02215.
Improving the spatial resolution of a fluorescence microscope has been an ongoing challenge in the imaging community. To address this challenge, a variety of approaches have been taken, ranging from instrumentation development to image post-processing. An example of the latter is deconvolution, where images are numerically deblurred based on a knowledge of the microscope point spread function.
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