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Untrained Position-Encoded Multilayer Perceptron Network for Structured Illumination Microscopy Reconstruction.

Sahil Sharma¹, Leonidas Zimianitis², Krishnendu Samanta^1,3

¹Department of Physics, Indian Institute of Technology Delhi, New Delhi 110016, India.

Chemical & Biomedical Imaging

|June 26, 2026

Summary

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This summary is machine-generated.

A new Position Encoded Multi-Layer Perceptron (PEM) network reconstructs super-resolution images using structured illumination microscopy (SIM) without training data. This data-efficient method offers robust, high-resolution imaging adaptable to various microscopy setups.

Area of Science:

Microscopy
Computational Imaging
Biophysics

Background:

Structured Illumination Microscopy (SIM) achieves super-resolution by encoding spatial details with patterned light.
Conventional Fourier-based SIM reconstruction methods can produce artifacts, while deep learning approaches often require extensive training data and lack flexibility.
Existing methods struggle with adaptability across different imaging setups and suboptimal imaging conditions.

Purpose of the Study:

To develop a novel, data-efficient method for reconstructing super-resolution images from SIM data.
To overcome limitations of traditional and deep learning-based reconstruction techniques in SIM.
To enable robust and adaptable super-resolution imaging without the need for large training datasets.

Main Methods:

Keywords:

SIM deep image prior machine learning physics informed neural networks super resolution

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Development of a Position Encoded Multi-Layer Perceptron (PEM) network utilizing implicit neural representations (INRs).
Integration of a SIM forward model for iterative reconstruction optimization based on structural similarity loss.
Encoding spatial coordinates using multi-frequency sinusoidal functions for detailed feature representation.

Main Results:

PEM-SIM successfully reconstructs 2D and 3D super-resolution images with fewer input frames compared to conventional methods.
The method demonstrates robustness across varying signal-to-noise ratios and performs comparably to standard algorithms on synthetic and experimental data.
PEM-SIM effectively predicts missing axial planes in 3D SIM datasets.

Conclusions:

PEM-SIM offers a data-efficient and adaptable solution for super-resolution imaging in microscopy.
The method eliminates the need for large training datasets, making advanced reconstruction more accessible.
This approach provides a flexible alternative for high-resolution imaging in diverse microscopy applications.