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D‑Blur: A Deep Learning Approach for Mapping Subdiffraction Diffusion with Motion-Blurred Images.

Dongyu Fan¹, Nikita Kovalenko², Jagriti Chatterjee²

¹Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.

Chemical & Biomedical Imaging

|December 26, 2025

Summary

This summary is machine-generated.

D-Blur, a novel algorithm, enhances single-particle tracking (SPT) by analyzing motion-blurred images. It accurately predicts diffusion coefficients, enabling detailed analysis of molecular dynamics in complex systems.

Keywords:

U-Net deep learning diffusion motion blur particle localization single-molecule microscopy

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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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Published on: September 5, 2019

Area of Science:

Biophysics
Materials Science
Image Analysis

Background:

Single-particle tracking (SPT) is vital for studying molecular transport but is limited by motion blur and low signal-to-noise ratio (SNR).
Conventional SPT methods struggle with localization accuracy, short trajectories, and fast-moving particles, hindering analysis in complex environments.
Existing techniques often require trajectory linking, which can introduce errors and is unsuitable for short or rapid movements.

Purpose of the Study:

To develop a robust algorithm, D-Blur, for accurate single-particle localization and diffusion coefficient (D) prediction from motion-blurred images.
To overcome the limitations of traditional SPT methods, particularly in scenarios with fast-moving particles and confined transport.
To enable the reconstruction of diffusion maps in heterogeneous systems without relying on trajectory linking.

Main Methods:

Development of D-Blur, a U-Net-based convolutional neural network (CNN) algorithm.
Localization of single particles and prediction of diffusion coefficients (D) directly from motion-blurred point spread functions (PSFs).
Validation using simulated emitters in heterogeneous environments and experimental data of free diffusers.

Main Results:

D-Blur successfully localizes single particles and predicts diffusion coefficients (D) from motion-blurred PSFs.
The algorithm enables the reconstruction of diffusion maps for confined transport in porous materials.
Validation demonstrated accuracy with both simulated and experimental data, outperforming conventional SPT limitations.

Conclusions:

D-Blur offers a powerful solution for analyzing molecular dynamics directly from microscopy images, bypassing the need for trajectory linking.
The algorithm enhances diffusion analysis in complex biological and porous material systems.
This work lays the groundwork for advanced diffusion mapping and future applications in microscopy-based studies.