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

ABSTRACT: Image-based single-particle tracking (SPT) provides insight into complex transport within diverse biological and porous material structures, but its performance is constrained by motion blur and a low signal-to-noise ratio (SNR). Traditional SPT methods are sensitive to localization errors and often struggle with short trajectories and fast-moving emitters. In this work, we develop D-Blur, a U-Net-based convolutional neural network (CNN) algorithm designed to localize single particles and predict their diffusion coefficients (D) from motion-blurred point spread functions (PSFs). The obtained D values of emitters enable the reconstruction of diffusion maps on confined transport in porous materials. We validate the algorithm with simulated emitters in a heterogeneous environmen

SUBMITTER: Fan D 

PROVIDER: S-EPMC12728762 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature