Paper
23 February 2018 Preprocessing method to correct illumination pattern in sinusoidal-based structured illumination microscopy
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Abstract
The restored images in structured illumination microscopy (SIM) can be affected by residual fringes due to a mismatch between the illumination pattern and the sinusoidal model assumed by the restoration method. When a Fresnel biprism is used to generate a structured pattern, this pattern cannot be described by a pure sinusoidal function since it is distorted by an envelope due to the biprism’s edge. In this contribution, we have investigated the effect of the envelope on the restored SIM images and propose a computational method in order to address it. The proposed approach to reduce the effect of the envelope consists of two parts. First, the envelope of the structured pattern, determined through calibration data, is removed from the raw SIM data via a preprocessing step. In the second step, a notch filter is applied to the images, which are restored using the well-known generalized Wiener filter, to filter any residual undesired fringes. The performance of our approach has been evaluated numerically by simulating the effect of the envelope on synthetic forward images of a 6-μm spherical bead generated using the real pattern and then restored using the SIM approach that is based on an ideal pure sinusoidal function before and after our proposed correction method. The simulation result shows 74% reduction in the contrast of the residual pattern when the proposed method is applied. Experimental results from a pollen grain sample also validate the proposed approach.
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H. Shabani, A. Doblas, G. Saavedra, and C. Preza "Preprocessing method to correct illumination pattern in sinusoidal-based structured illumination microscopy ", Proc. SPIE 10499, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXV, 104991Z (23 February 2018); https://doi.org/10.1117/12.2291013
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KEYWORDS
Modulation

Microscopy

Diffraction

Fourier transforms

3D image processing

3D image reconstruction

Linear filtering

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