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A Frequency-Aware Self-Supervised Framework for MEMS-OCT Denoising.

Gaolin Zhang1, Zonghao Li1, Hui Zhao1,2

  • 1School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.

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Summary
This summary is machine-generated.

This study introduces FEN2N, a novel deep learning method for denoising Optical Coherence Tomography (OCT) images. FEN2N significantly improves image quality by reducing speckle noise while preserving crucial details for accurate biomedical analysis.

Keywords:
OCT denoisingfrequency domainself-supervised

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Area of Science:

  • Biomedical Imaging
  • Optical Coherence Tomography (OCT)
  • Image Processing

Background:

  • Optical Coherence Tomography (OCT) is vital for biomedical detection but susceptible to speckle noise, especially in endoscopic systems.
  • Existing denoising methods struggle with paired data requirements and modeling global image structures.

Purpose of the Study:

  • To develop an advanced deep learning framework for effective speckle noise reduction in OCT images.
  • To enhance the accuracy and visual quality of OCT imaging for clinical applications.

Main Methods:

  • Proposed a frequency-domain enhanced UNet based on the Neighbor2Neighbor (N2N) framework, named FEN2N.
  • Integrated wavelet-guided spectral pooling modules (WSPMs) and frequency-domain enhanced receptive field blocks (FE-RFBs).
  • Applied FEN2N to OCT images acquired from a self-constructed micro-electro-mechanical system (MEMS)-OCT system.

Main Results:

  • FEN2N demonstrated a >2.3 dB PSNR improvement over the N2N baseline.
  • The FE-RFB component improved the Structural Similarity Index Measure (SSIM) by 0.02.
  • FEN2N outperformed state-of-the-art methods in speckle suppression and detail preservation.

Conclusions:

  • FEN2N effectively suppresses speckle noise in OCT images.
  • The method preserves fine structural details essential for clinical diagnosis.
  • FEN2N offers a significant advancement for OCT image analysis in biomedical applications.