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Related Experiment Video

Updated: Apr 19, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
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Super-Resolution Microscopy for Precision Microsphere Defect Inspection Using Sparrow-Optimized Autocorrelation

Tao He1, Jiaxin Yu1, Liwei Ou1

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Annals of the New York Academy of Sciences
|April 17, 2026
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Summary

This study introduces a super-resolution imaging method for detecting defects on precision microspheres. The new technique significantly improves resolution and processing speed, aiding in surface roughness assessment.

Keywords:
autocorrelation two‐step deconvolutionimage reconstructionsparrow algorithmsuper‐resolution

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Area of Science:

  • Optics and Photonics
  • Materials Science
  • Image Processing

Background:

  • Detecting surface defects on precision microspheres is challenging due to their small size.
  • Traditional optical microscopy suffers from low resolution and blurriness, limiting defect detection accuracy.

Purpose of the Study:

  • To develop a super-resolution image reconstruction method for enhanced detection of surface defects on precision microspheres.
  • To improve image resolution and reduce artifacts in microsphere surface defect analysis.

Main Methods:

  • Proposed an autocorrelation two-step deconvolution super-resolution method.
  • Integrated the sparrow search algorithm for adaptive parameter optimization.
  • Utilized rolling Fourier ring correlation and FWHM for image quality assessment.

Main Results:

  • Achieved a resolution enhancement of 2.79 to 3.82 times compared to traditional methods.
  • Increased image processing speed by 6.3%-34.2% for equivalent frame counts.
  • Reconstructed more detailed features than deep learning models, aiding defect detection.

Conclusions:

  • The proposed method significantly enhances resolution and processing speed for microsphere surface defect detection.
  • The technique offers a valuable tool for quantitative assessment of surface roughness in precision microspheres.
  • This approach shows potential for improved defect analysis in micro-scale metrology.