Speckle reduction based on fractional-order filtering and boosted singular value shrinkage for optical coherence tomography image

Abstract

Optical coherence tomography (OCT) is a micrometer-resolution optical imaging technology that has been widely used in many fields, such as medicine and materials science. However, OCT images inevitably suffer from speckle noise which obscures the structural information of image. To remove speckle noise, a boosted singular value shrinkage algorithm based on fractional-order filtering is proposed in this paper. An OCT image is first divided into many overlapping image blocks and each block is filtered using a fractional mask, and then an absolute distance is used as a similarity criterion for block matching to form a low rank group matrix. Furthermore, the fractional-order preprocessing is performed on the group matrix. Finally, singular value decomposition, a piecewise Laplace shrinkage, aggregating and boosted iterative regularization technique are used to reconstruct a filtered image. Extensive experiments are performed on 18 OCT images of the retina of the human eye to verify the validity of the proposed method. Experimental results show that the proposed method harvests best PSNR, SSIM and EP results in most cases. In addition, results of the paired samples t-test show that the proposed method can remove noise more thoroughly and better preserve the structural information of the OCT images. In summary, the proposed algorithm provides better objective metrics and visual inspection compared with several state-of-the-art denoising algorithms.