# Sub2Full: split spectrum to boost OCT despeckling without clean data

arXiv: https://arxiv.org/abs/2401.10128 · HTML: https://mlsanity.com/paper/2401.10128/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Resolution enhancement and realistic speckle recovery with generative adversarial modeling of micro-optical coherence tomography](https://mlsanity.com/paper/2003.06035/) (2020)

## Built on by (most cited first, 3 of 3)

- [Rumor Detection with a novel graph neural network approach](https://mlsanity.com/paper/2403.16206/) (2024)
- [Development and Application of a Monte Carlo Tree Search Algorithm for Simulating Da Vinci Code Game Strategies](https://mlsanity.com/paper/2403.10720/) (2024)
- [EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction](https://mlsanity.com/paper/2403.10695/) (2024)

## Abstract (arXiv)

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.