Novel Reconstruction With Inter-Frame Motion Compensation For Fast Super-Resolution Live Cell Imaging

Structured illumination microscopy is a widely popular super-resolution technique for live cell imaging capable of surpassing the diffraction limit. Its temporal resolution is limited by the need to capture multiple low-resolution images to reconstruct a single high-resolution image. When observing rapid biological processes, the local movement between frames leads to the formation of reconstruction artifacts, which subsequently impair the data interpretation. We propose to include this type of movement in the definition of the image formation forward problem. The motion can then be estimated from the original data using optical flow, and the optimization problem is solved using the alternating direction method of multipliers. Our approach is tested against other reconstruction techniques on both synthetic and real biological data.

[1]  P. Evans,et al.  FCHO controls AP2’s initiating role in endocytosis through a PtdIns(4,5)P2-dependent switch , 2022, Science advances.

[2]  A. Rohrbach,et al.  Fast TIRF-SIM imaging of dynamic, low-fluorescent biological samples. , 2020, Biomedical optics express.

[3]  Walter Müller,et al.  Automated distinction of shearing and distortion artefacts in structured illumination microscopy. , 2018, Optics express.

[4]  Nelly Pustelnik,et al.  Nonsmooth Convex Optimization for Structured Illumination Microscopy Image Reconstruction , 2018, Inverse problems.

[5]  K. Khare,et al.  Accurate estimation of the illumination pattern's orientation and wavelength in sinusoidal structured illumination microscopy. , 2018, Applied optics.

[6]  Laura Waller,et al.  Structured illumination microscopy with unknown patterns and a statistical prior , 2016, Biomedical optics express.

[7]  Jie Yin,et al.  Image reconstruction for structured-illumination microscopy with low signal level. , 2014, Optics express.

[8]  Anne Sentenac,et al.  Structured illumination microscopy using unknown speckle patterns , 2012, Nature Photonics.

[9]  Vincent Loriette,et al.  Bayesian Estimation for Optimized Structured Illumination Microscopy , 2012, IEEE Transactions on Image Processing.

[10]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[11]  A. Stemmer,et al.  Structured illumination in total internal reflection fluorescence microscopy using a spatial light modulator. , 2008, Optics letters.

[12]  R. Heintzmann,et al.  Superresolution by localization of quantum dots using blinking statistics. , 2005, Optics express.

[13]  Gunnar Farnebäck,et al.  Two-Frame Motion Estimation Based on Polynomial Expansion , 2003, SCIA.

[14]  M. Gustafsson Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy , 2000, Journal of microscopy.

[15]  S. Hell,et al.  Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. , 1994, Optics letters.

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

[17]  D. Axelrod Cell-substrate contacts illuminated by total internal reflection fluorescence , 1981, The Journal of cell biology.

[18]  J. Moreau Proximité et dualité dans un espace hilbertien , 1965 .

[19]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[20]  E. Abbe Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung , 1873 .