Temporal and volumetric denoising via quantile sparse image prior
This addresses denoising for medical imaging modalities like OCT and CT, which is an incremental improvement with a novel method for known bottlenecks.
The paper tackled denoising in medical images by introducing a universal regularization term called the quantile sparse image (QuaSI) prior, and demonstrated its effectiveness on volumetric OCT and CT data with excellent performance.
This paper introduces an universal and structure-preserving regularization term, called quantile sparse image (QuaSI) prior. The prior is suitable for denoising images from various medical imaging modalities. We demonstrate its effectiveness on volumetric optical coherence tomography (OCT) and computed tomography (CT) data, which show different noise and image characteristics. OCT offers high-resolution scans of the human retina but is inherently impaired by speckle noise. CT on the other hand has a lower resolution and shows high-frequency noise. For the purpose of denoising, we propose a variational framework based on the QuaSI prior and a Huber data fidelity model that can handle 3-D and 3-D+t data. Efficient optimization is facilitated through the use of an alternating direction method of multipliers (ADMM) scheme and the linearization of the quantile filter. Experiments on multiple datasets emphasize the excellent performance of the proposed method.