  Optical coherence tomography (OCT) is an important interferometric diagnostic
technique which provides cross-sectional views of the subsurface microstructure
of biological tissues. However, the imaging quality of high-speed OCT is
limited due to the large speckle noise. To address this problem, this paper
proposes a multi-frame algorithmic method to denoise OCT volume.
Mathematically, we build an optimization model which forces the temporally
registered frames to be low rank, and the gradient in each frame to be sparse,
under logarithmic image formation and noise variance constraints. Besides, a
convex optimization algorithm based on the augmented Lagrangian method is
derived to solve the above model. The results reveal that our approach
outperforms the other methods in terms of both speckle noise suppression and
crucial detail preservation.

Computer Vision and Pattern Recognition