Sub2Full: split spectrum to boost OCT despeckling without clean data
Lingyun Wang Jose A Sahel Shaohua Pi
Optical coherence tomography (OCT) suffers from speckle noise, causing the
deterioration of image quality, especially in high-resolution modalities like
visible light OCT (vis-OCT). The potential of conventional supervised deep
learning denoising methods is limited by the difficulty of obtaining clean
data. Here, we proposed an innovative self-supervised strategy called Sub2Full
(S2F) for OCT despeckling without clean data. This approach works by acquiring
two repeated B-scans, splitting the spectrum of the first repeat as a
low-resolution input, and utilizing the full spectrum of the second repeat as
the high-resolution target. The proposed method was validated on vis-OCT
retinal images visualizing sublaminar structures in outer retina and
demonstrated superior performance over conventional Noise2Noise and Noise2Void
schemes. The code is available at
https://github.com/PittOCT/Sub2Full-OCT-Denoising.