Optical coherence tomography image despeckling based on tensor singular value decomposition and fractional edge...

Area of Science:

• Medical Imaging
• Signal Processing
• Computational Science

Background:

• Optical coherence tomography (OCT) is crucial for medical diagnosis.
• Speckle noise significantly degrades OCT image quality, hindering diagnostic accuracy.
• Effective noise reduction is vital for reliable OCT imaging.

Purpose of the Study:

• To develop an advanced speckle noise reduction technique for OCT images.
• To improve the quality and diagnostic utility of OCT imaging.
• To enhance image clarity while preserving structural details.

Main Methods:

• Speckle noise reduction modeled as a low-rank tensor approximation problem.
• Utilized tensor singular value decomposition (t-SVD) for noise suppression.
• Incorporated feature-guided thresholding and adaptive backward projection for enhanced results.

Main Results:

• The proposed algorithm effectively reduces speckle noise in OCT images.
• Demonstrated superior performance compared to existing methods in objective metrics.
• Successfully preserved image features and edges during the despeckling process.

Conclusions:

• The developed low-rank tensor approximation method significantly enhances OCT image quality.
• This technique offers a promising solution for improving diagnostic accuracy in OCT-based medical imaging.
• The algorithm provides effective speckle suppression with excellent edge preservation.