Abstract
In this paper, we propose a new approach for structured illumination microscopy image reconstruction. We first introduce the principles of this imaging modality and describe the forward model. We then propose the minimization of nonsmooth convex objective functions for the recovery of the unknown image. In this context, we investigate two data-fitting terms for Poisson–Gaussian noise and introduce a new patch-based regularization method. This approach is tested against other regularization approaches on a realistic benchmark. Finally, we perform some test experiments on images acquired on two different microscopes.
Highlights
Context Superresolution approaches allow us to go beyond the resolution of standard widefield fluorescence microscopy, breaking the classical diffraction limit defined by Abbe in 1873 [1]
We have proposed a new method for structured illumination image reconstruction
We have considered a primal–dual algorithm, which does not require the direct inversion of the forward operator, as this one is too large to be directly handled
Summary
Context Superresolution approaches allow us to go beyond the resolution of standard widefield fluorescence microscopy, breaking the classical diffraction limit defined by Abbe in 1873 [1]. Structured illumination microscopy (SIM) is one of the recently proposed optical superresolution methods compatible with time lapse imaging of several labels. None of the recent regularization methods, such as the Schatten norm of the Hessian operator [13], nonlocal total variation (NLTV) [14,15,16], global patch dictionaries [17,18] or local patch dictionaries [19] have been applied to structured illumination reconstruction, limiting the final performance of this superresolution technique in its ability to discriminate fine structures of interest. Contribution & organization of the article We propose here a reconstruction method taking into account the Poisson–Gaussian distribution of the noise and relying on a new regularization approach based on learning local dictionaries of patches in a convex setting. In B, we recall the implementation details for the tested regularization cost terms and the needed tools for their minimization
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