{"id":764,"date":"2023-07-29T12:52:39","date_gmt":"2023-07-29T16:52:39","guid":{"rendered":"https:\/\/sites.bu.edu\/biomicroscopy\/?page_id=764"},"modified":"2023-11-21T19:28:59","modified_gmt":"2023-11-22T00:28:59","slug":"deblurring-by-pixel-reassignment","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/biomicroscopy\/research\/deblurring-by-pixel-reassignment\/","title":{"rendered":"Deblurring by pixel reassignment"},"content":{"rendered":"

Summary:<\/strong> 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.<\/p>\n