QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data

TL;DR

This paper presents QuaSI, a novel variational denoising method for retinal OCT images that effectively reduces speckle noise while preserving important structures, outperforming existing techniques with fewer B-scans.

Contribution

Introduction of QuaSI, a quantile sparse image prior, combined with median filter regularization and ADMM for efficient spatio-temporal OCT denoising.

Findings

Achieved comparable denoising with 4 B-scans versus 13 B-scans averaging.

Outperformed current state-of-the-art OCT denoising methods.

Effectively preserved diagnostic image structures.

Abstract

Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise. This paper introduces a spatio-temporal denoising algorithm for OCT data on a B-scan level using a novel quantile sparse image (QuaSI) prior. To remove speckle noise while preserving image structures of diagnostic relevance, we implement our QuaSI prior via median filter regularization coupled with a Huber data fidelity model in a variational approach. For efficient energy minimization, we develop an alternating direction method of multipliers (ADMM) scheme using a linearization of median filtering. Our spatio-temporal method can handle both, denoising of single B-scans and temporally consecutive B-scans, to gain volumetric OCT data with enhanced signal-to-noise ratio. Our algorithm based on 4 B-scans only achieved comparable performance to averaging 13 B-scans and outperformed other current denoising methods.