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Total variation regularized tensor ring decomposition for OCT image denoising and super-resolution.

Parisa Ghaderi Daneshmand1, Hossein Rabbani1

  • 1Medical Image & Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran.

Computers in Biology and Medicine
|May 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new TRFOTTV model to improve optical coherence tomography (OCT) image quality by reducing noise and increasing resolution. The method enhances OCT image analysis for better medical diagnoses.

Keywords:
Alternative direction method of multipliers (ADMM)Artificial intelligenceFirst-order tensor-based total variationOptical coherence tomography (OCT)Proximal alternating minimization (PAM)Super-resolutionTensor-ring decomposition

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Biomedical Engineering

Background:

  • Optical Coherence Tomography (OCT) imaging suffers from noise and low sampling rates, hindering accurate diagnosis.
  • Existing methods struggle to effectively address both noise and resolution issues simultaneously in OCT data.

Purpose of the Study:

  • To develop a novel hybrid model for simultaneous super-resolution and noise suppression in OCT images.
  • To enhance the diagnostic accuracy of OCT by improving image quality.

Main Methods:

  • A hybrid Tensor-Ring (TR) decomposition and First-Order Tensor-Based Total Variation (FOTTV) model (TRFOTTV) was proposed.
  • Nonlocal 3D patches were extracted and grouped into low-rank tensors for TR decomposition.
  • FOTTV was integrated to preserve spatial smoothness and layer structures.

Main Results:

  • The TRFOTTV model demonstrated superior performance in visual and numerical assessments.
  • The proposed method effectively suppressed noise and enhanced the resolution of OCT images.
  • Validation was performed on four diverse OCT datasets.

Conclusions:

  • The TRFOTTV model offers a significant advancement in OCT image processing.
  • This technique improves image quality, potentially leading to more reliable OCT-based diagnoses.
  • The hybrid approach effectively balances noise reduction and super-resolution tasks.