Of the super-resolution microscopy techniques, structured illumination microscopy (SIM) is arguably the most counterintuitive to grasp. Of course, that’s what makes it so much fun!
To understand how it works we’ll have to think about the nature of information and how it can move between real and Fourier space. Further, we’ll discuss the mechanics of generating the structured illumination lightpath, and how the super-resolution images are reconstructed.
The Moiré Effect
Here is a quick age test: do you remember when TV presenters wore ties that seemed to create funny patterns on the screen? Well, if you have seen this, you already know something about the principles of structured illumination microscopy (SIM). Presenters and producers have been more conscious about this for the last several decades, but up to the 70s, this bizarre phenomenon could sometimes be witnessed.
What was happening in those images was that the apparent size of certain details in the image was very close to the screen resolution, or the pixel size. This near-miss coincidence of two patterns, the one in the image and the one on the screen itself, created a third pattern, which changed as the relative position of the two other patterns changed. This phenomenon is called a Moiré effect and is, in fact, the optical equivalent of the sound beats caused by interference between two sound waves of similar frequencies.
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The Moiré effect is not just there to annoy TV crews and viewers – it can also be used to benefit microscopists. SIM is a widefield microscopy technique that uses this same Moiré effect to extract higher resolution information as compared to standard, diffraction-limited microscopy.
| Advantages | Disadvantages |
|---|---|
| Compatible with all conventional fluorophores and live-cell applications. | Only moderate lateral resolution improvement compared to other super-resolution methods. |
| Fast recordings of a large field-of-view. | Length of processing time (1 to 30 seconds) is necessary to achieve high-resolution images. |
| Very high sensitivity and contrast compared to widefield microscopy. | Errors in grating position, system calibration, refractive index mismatch, and/or poor sample quality can introduce artifacts and severely compromise the final result. |
| 3D sectioning of thin samples (e.g., cells). | Not appropriate for use with thick samples, although algorithms to overcome this problem do exist. |
How Does Structured Illumination Microscopy Work?
As discussed SIM (structured illumination microscopy) excites the sample with a patterned light field rather than uniform illumination. When this fine excitation pattern overlaps with sub-diffraction detail in the sample, the two interfere to produce Moiré patterns.
Those Moiré fringes encode high-frequency spatial information that a conventional, diffraction-limited microscope cannot resolve directly. By recording the emitted fluorescence as the pattern is moved and reoriented, hidden information can later be recovered computationally.
Generating the pattern (the optics)
A polarized laser beam passes through a transmitting phase grating — a glass plate with etched or coated stripes — which splits it into multiple diffraction orders leaving at different angles. The three central beams (the 0, +1 and −1 orders) are recombined in the microscope’s focal plane to form the fine striped illumination. Two beams are enough to structure the pattern in X and Y; the third, central beam is what allows structuring in Z for 3D SIM.
Acquiring the images
To extract the encoded detail, the pattern is sampled at many positions relative to the sample:
- It is shifted laterally in five steps (five phases), usually by physically translating the grating.
- It is rotated to three different angles, usually by physically rotating the grating.
That gives 15 raw images per focal plane (5 phases × 3 angles). The sample is then stepped through the focal plane — typically 125 nm between sections — to build a z-stack, as in other optical-sectioning methods.
Reconstruction (the math)
The algorithm combines all 15 images of a section in Fourier space. Because each image has a different pattern position, their Fourier representations can be arranged so that high-frequency sample information — normally inaccessible — is teased out. This depends on knowing the pattern’s own Fourier signature, the Optical Transfer Function (OTF), which plays a role analogous to the Point Spread Function (PSF) used in conventional deconvolution.
The recovered high-frequency information is then shifted back into its correct position and inverse-transformed into a recognizable image of the sample. The algorithm also draws on the sections immediately above and below, which improves resolution along Z as well as laterally.
Resolution achieved
The result is roughly double the lateral resolution of a diffraction-limited instrument — about 100–130 nm. In 3D SIM, modulating the illumination in all three dimensions roughly doubles axial resolution too, giving about 250–350 nm in Z. Overall this corresponds to an ~8-fold reduction in the smallest resolvable volume (a 2× gain in each dimension).
Resolution can be pushed further by using more rotation angles, but the gains are bounded by grid frequency, the number of angles, and the geometric relationship between the stripes and the sample features — and they come at the cost of slower acquisition and more photobleaching.
| It’s a digital camera, so we only collect amplitude | Structural information of any real image (such as your sample) can be described as a mix of sinusoidal modulations in the amplitude of the light- in other words, a wave form which varies in height. In truth, it is also a mixture of phase values, spectra, and quantum effects, but since our readout is a digital camera we can only collect amplitude values. |
| The low and high frequencies are invisible | The information in these amplitude modulations may also be considered as frequencies through use of a Fourier Transform (FT) – visible or diffraction-limited structures are in a certain frequency range, while frequencies in the lower and higher ranges are obscured and normally invisible. By this framework, very small structures in real space (e.g. sub-diffraction details in your sample <200 nm in size) are represented by very high frequencies. |
| Patterns of illumination equal encoded information | The beauty of SIM is that by creating patterns in the illuminating light and thus the frequency patterns of light emitted from the sample, high frequency information becomes encoded in those patterns. This high frequency information, and therefore the sub-diffraction structures, can be extracted by deconvolving the patterns in light emitted from the sample from the patterns you introduced to the illumination. |
| Unique patterns also equal a reconstructed image | Normally, these higher frequency emissions from the sample would be unable to enter the microscopes’ back focal plane and cause smearing in the image. By applying many unique patterns to the same area of sample, SIM reconstructs where those emissions would come from, allowing us to see structures which are up to 100 nm below the diffraction limit. |
Originally published November 23, 2017. Reviewed and revised on 23rd June 2026.
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