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A Robust Reconstruction and Pattern Calibration Framework for Structured Illumination Microscopy

N. Chu, R. Nussbaumer, L. Donati, D. Fortun, D. Sage, M. Unser

2016 Quantitative BioImaging Conference (QBIC'16), Delft, Kingdom of the Netherlands, January 13-15, 2016.



Structured illumination microscopy (SIM) is an effective and widely used method for producing high-resolution fluorescence micrographs. This imaging technique can reach up to twice the lateral resolution of conventional wide-field microscopy [1]. In SIM, the sample is imaged with varying configurations of an illumination pattern and a high-resolution image can be reconstructed from the collected data. The quality of the reconstructed SIM image is not only dependent upon the type of reconstruction method used, but also upon the correct setup of the illumination pattern. By taking advantage of recent advances in mathematical imaging and sparse signal recovery [2], [3], we have designed a fast iterative algorithm that imposes sparsity constraints on the Hessian of the image. By doing so, we are able to outperform the current linear reconstruction methods (multichannel Wiener filter), while avoiding the staircase artifacts of total variation regularization.

References

  1. M.G.L. Gustafsson, "Surpassing the Lateral Resolution Limit by a Factor of Two Using Structured Illumination Microscopy," Journal of Microscopy, vol. 198, no. 2, pp. 82-87, May 2000.

  2. E. Bostan, U.S. Kamilov, M. Nilchian, M. Unser, "Sparse Stochastic Processes and Discretization of Linear Inverse Problems," IEEE Transactions on Image Processing, vol. 22, no. 7, pp. 2699-2710, July 2013.

  3. S. Lefkimmiatis, J.P. Ward, M. Unser, "Hessian Schatten-Norm Regularization for Linear Inverse Problems," IEEE Transactions on Image Processing, vol. 22, no. 5, pp. 1873-1888, May 2013.


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