This repository contains a Python simulation of Structured Illumination Microscopy (SIM).
The goal is to demonstrate the key concepts of SIM — how structured patterns shift high-frequency information into the passband, and how computational reconstruction recovers resolution beyond the diffraction limit.
The simulation includes:
- Generation of a synthetic ground truth object
- Widefield (low-pass filtered) imaging
- Structured illumination (multiple orientations & phases)
- Fourier domain analysis (central + sidebands)
- Reconstruction with subpixel Fourier shifting
- Noise modeling (Gaussian & Poisson)
- Wiener-like weighting for noise suppression
- Final comparison of clean vs noisy reconstructions
- Figures with automatic scale bars
- Run the simulation:
- python main.py
- This will:
Generate a synthetic test object.
Perform SIM image simulation and reconstruction.
Display Fourier spectra, frequency components, and reconstructed images.
Show both clean and noisy reconstructions with scale bars.
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Why multiple orientations and phases are required for isotropic resolution.
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How sidebands appear in Fourier space and why they must be shifted back.
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How subpixel Fourier shifts and Wiener filtering enable robust reconstruction.
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Effect of Gaussian (readout noise) and Poisson (shot noise) on image quality.
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Trade-off between resolution and signal-to-noise ratio in microscopy.
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Gained practical understanding of SIM principles.
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Learned how computational processing (Fourier analysis, Wiener filtering) enables super-resolution.
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Observed the robustness of SIM reconstruction under realistic noise conditions.
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Connected mathematical concepts (Fourier optics) to real microscopy applications.
