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HIST-DIP: histogram thresholding and deep image priors assisted smartphone-based fluorescence microscopy imaging.

Harshitha Govindaraju1, Muhammad Nabeel Tahir1, Umer Hassan1

  • 1Rutgers, The State University of New Jersey, Dept of Electrical and Computer Engineering, 94 Bret Rd, Piscataway, 08854 USA. umer.hassan@rutgers.edu.

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HIST-DIP enhances smartphone fluorescence microscopy images by removing noise and improving clarity. This unsupervised method boosts image quality for accessible, real-time point-of-care diagnostics without needing training data.

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Area of Science:

  • Biomedical Imaging
  • Computational Microscopy
  • Image Processing

Background:

  • Smartphone-based fluorescence microscopy offers low-cost, portable point-of-care diagnostics.
  • Image quality issues like noise and blur limit current applications.
  • Restoration methods often require large labeled datasets, hindering real-time use.

Purpose of the Study:

  • To develop an unsupervised image restoration framework for smartphone fluorescence microscopy.
  • To improve image quality (contrast, resolution, signal-to-noise ratio) without external training data.
  • To enable enhanced real-time, low-cost diagnostic imaging.

Main Methods:

  • Introduced HIST-DIP (HIStogram Thresholding and Deep Image Prior) framework.
  • Utilized histogram thresholding to isolate fluorescence signals and remove background noise.
  • Employed Deep Image Prior (DIP) for structural refinement and resolution enhancement.

Main Results:

  • Significant improvements in image quality metrics: PSNR increased from 15.59 to 27.10 dB.
  • SSIM improved from 0.035 to 0.82, indicating better structural similarity.
  • Enhanced contrast-to-noise ratio (CNR) and signal-difference-to-noise ratio (SDNR) for sharper details.

Conclusions:

  • HIST-DIP effectively restores fluorescence microscopy images from smartphones.
  • The unsupervised approach eliminates the need for training datasets, ideal for point-of-care applications.
  • This method holds potential for real-time, on-device diagnostic imaging enhancement.