Application of RPCA in optical coherence tomography for speckle noise reduction

Optical coherence tomography (OCT) is a promising technology, which could be used in a variety of imaging applications. However, OCT images are usually degraded by speckle noise. Speckle noise reduction in OCT is particularly challenging because it is difficult to separate the noise and the information components in the speckle pattern. In this study, a novel speckle noise reduction technique, based on robust principal component analysis (RPCA), is presented and applied to OCT images for the first time. The proposed technique gives an optimal estimate of OCT image domain transformations such that the matrix of transformed OCT images can be decomposed as the sum of a sparse matrix of speckle noise and a low-rank matrix of the denoised image. The decomposition is a unique feature of the proposed method which can not only reduce the speckle noise, but also preserve the structural information about the imaged object. Applying the proposed technique to a number of OCT images showed significant improvement of image quality.