engleski

Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography

Speckle artifact can strongly hamper quantitative analysis of optical coherence tomography (OCT) which is necessary to provide assessment of ocular disorders associated with vision loss. Here, we introduce new method for speckle reduction, which leverages from low-rank + sparsity decomposition (LRpSD) of logarithm of intensity OCT images. In particular, we combine nonconvex regularization-based low-rank approximation of original OCT image with sparsity term that incorporates the speckle. State-of-the-art methods for LRpSD require a priori knowledge of a rank and approximate it with nuclear norm which is not accurate rank indicator. As opposed to that, proposed method provides more accurate approximation of a rank through the use of nonconvex regularization that induces sparse approximation of singular values. Furthermore, a rank value is not required to be known a priori. This, in turn, yields automatic and computationally more efficient method for speckle reduction which yields OCT image with improved contrast-to-noise ratio, contrast and edge fidelity. The source code will be available at www.mipav.net/English/research/research.html.

optical coherence tomography ; speckle ; low-rank + sparsity decomposition ; nonconvex regularization

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