Optical coherence tomography image despeckling based on saliency enhancement and high-order singular value Marchenko-Pastur truncation

Abstract Optical Coherence Tomography (OCT) is a high-resolution imaging technique extensively used in various fields, including medical diagnosis. The presence of speckle noise significantly degrades the quality of OCT images. To address this, a novel denoising approach based on High-Order Singular Value Decomposition (HOSVD), along with saliency enhancement and Marchenko-Pastur (MP) truncation, has been proposed. Initially, the method leverages the weighted absolute distance based on variance and information entropy to locate non-local patches that are highly correlated with a given reference block. Following this, a three-dimensional tensor is decomposed via HOSVD and then contracted based on the principles from the MP theorem in random matrix theory. An iterative regularization technique, coupled with a saliency enhancement strategy, is also employed to improve the denoising performance. Experimental results show that the method is comparable to existing advanced denoising algorithms in terms of reducing speckle noise and preserving image details.