  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.
