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Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Variational bayesian super resolution.

S Derin Babacan¹, Rafael Molina, Aggelos K Katsaggelos

¹Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, IL 61801, USA. dbabacan@illinois.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 30, 2010

Summary

This summary is machine-generated.

This study introduces novel super resolution (SR) methods that simultaneously estimate high-resolution images and motion parameters. These Bayesian algorithms enhance image quality by preventing error propagation and are robust to motion estimation inaccuracies.

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Super-resolution Imaging of Neuronal Dense-core Vesicles

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Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Super-resolution Imaging of Neuronal Dense-core Vesicles

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Super-resolution (SR) aims to reconstruct a high-resolution (HR) image from multiple low-resolution (LR) images.
Accurate sub-pixel motion estimation is critical for effective SR performance.
Existing methods often struggle with error propagation and parameter tuning.

Purpose of the Study:

To develop novel SR methods for simultaneous HR image and motion parameter estimation.
To improve the accuracy and robustness of SR image reconstruction.
To create fully automated SR algorithms without manual parameter tuning.

Main Methods:

A Bayesian formulation models the HR image, acquisition process, motion, and model parameters stochastically.
Variational Bayesian analysis is employed to jointly estimate distributions of all unknowns.
The proposed motion estimation is a stochastic generalization of the Lucas-Kanade algorithm.

Main Results:

The proposed framework incorporates uncertainty to prevent error propagation between estimates.
Algorithms demonstrate robustness to errors in motion parameter estimation.
Experimental results show superior performance compared to state-of-the-art SR algorithms.

Conclusions:

The developed Bayesian SR methods effectively reconstruct HR images from LR inputs.
Simultaneous estimation of HR images and motion parameters enhances robustness and automation.
The approach offers a significant advancement in super-resolution imaging.