QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data
This addresses noise reduction in medical imaging for retinal diagnostics, but it is incremental as it builds on existing variational and regularization techniques.
The paper tackled speckle noise in retinal OCT imaging by introducing a spatio-temporal denoising algorithm using a quantile sparse image prior, achieving comparable performance to averaging 13 B-scans with only 4 B-scans and outperforming other methods.
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.