Image reconstruction for structured-illumination microscopy with low signal level.

We report a new image processing technique for the structured illumination microscopy designed to work with low signals, with the goal of reducing photobleaching and phototoxicity of the sample. Using a pre-filtering process to estimate experimental parameters and total variation as a constraint to reconstruct, we obtain two orders of magnitude of exposure reduction while maintaining the resolution improvement and image quality compared to a standard structured illumination microscopy. The algorithm is validated on both fixed and live cell data with results confirming that we can image more than 15x more time points compared to the standard technique.

[1]  O. Mandula,et al.  Structured illumination microscopy of a living cell , 2009, 2011 International Quantum Electronics Conference (IQEC) and Conference on Lasers and Electro-Optics (CLEO) Pacific Rim incorporating the Australasian Conference on Optics, Lasers and Spectroscopy and the Australian Conference on Optical Fibre Technology.

[2]  Frank Cichos,et al.  Power-law intermittency of single emitters , 2007 .

[3]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[4]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[5]  Josiane Zerubia,et al.  Richardson–Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution , 2006, Microscopy research and technique.

[6]  Reto Fiolka,et al.  Phase optimisation for structured illumination microscopy. , 2013, Optics express.

[7]  Sapna A. Shroff,et al.  Phase-shift estimation in sinusoidally illuminated images for lateral superresolution. , 2009, Journal of the Optical Society of America. A, Optics, image science, and vision.

[8]  Mark Bates,et al.  Three-Dimensional Super-Resolution Imaging by Stochastic Optical Reconstruction Microscopy , 2008, Science.

[9]  A. Diaspro,et al.  Live-cell 3D super-resolution imaging in thick biological samples , 2011, Nature Methods.

[10]  M. Gustafsson,et al.  Super-resolution 3D microscopy of live whole cells using structured illumination , 2011, Nature Methods.

[11]  S. Hell,et al.  Subdiffraction resolution in far-field fluorescence microscopy. , 1999, Optics letters.

[12]  Gabriele Steidl,et al.  Deblurring Poissonian images by split Bregman techniques , 2010, J. Vis. Commun. Image Represent..

[13]  J. Lippincott-Schwartz,et al.  Imaging Intracellular Fluorescent Proteins at Nanometer Resolution , 2006, Science.

[14]  S. Weiss,et al.  Achieving increased resolution and more pixels with Superresolution Optical Fluctuation Imaging (SOFI) , 2010, Optics express.

[15]  M. Gustafsson,et al.  Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination. , 2008, Biophysical journal.

[16]  Wotao Yin,et al.  Error Forgetting of Bregman Iteration , 2013, J. Sci. Comput..

[17]  Junfeng Yang,et al.  A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..