Quantitative analysis of deep learning-based denoising model efficacy on optical coherence tomography images with different noise levels.

Journal: Photodiagnosis and photodynamic therapy
PMID: 37949385

Abstract

BACKGROUND: To quantitatively evaluate the effectiveness of the Noise2Noise (N2N) model, a deep learning (DL)-based noise reduction algorithm, on enhanced depth imaging-optical coherence tomography (EDI-OCT) images with different noise levels.

Authors

  • Furkan Kirik
    Department of Ophthalmology, Faculty of Medicine, Bezmialem Vakif University, Adnan Menderes (Vatan) Avenue, Fatih, Istanbul 34093, Turkiye. Electronic address: f.kirik21@gmail.com.
  • Farid Iskandarov
    Department of Ophthalmology, Faculty of Medicine, Bezmialem Vakif University, Adnan Menderes (Vatan) Avenue, Fatih, Istanbul 34093, Turkiye.
  • Kamile Melis Erturk
    Department of Ophthalmology, Faculty of Medicine, Bezmialem Vakif University, Adnan Menderes (Vatan) Avenue, Fatih, Istanbul 34093, Turkiye.
  • Hakan Ozdemir
    Department of Ophthalmology, Faculty of Medicine, Bezmialem Vakif University, Adnan Menderes (Vatan) Avenue, Fatih, Istanbul 34093, Turkiye.

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