Improved sparse representation algorithm for optical coherence tomography images

Abstract

AbstractAn improved sparse representation algorithm is proposed for reducing speckle noise in optical coherence tomography (OCT) images. To reduce the dictionary training time, the orthogonal matching pursuit algorithm (OMP) is replaced by a piecewise orthogonal matching pursuit algorithm (ST‐OMP), and a threshold standard is set to train multiple atoms simultaneously to improve the tracking efficiency. Normal, diabetic macular edema, and fundus neovascularization membrane retina OCT images were tested using the proposed algorithm and other denoising algorithms. The results show that the proposed algorithm exhibits an excellent denoising ability. The details and edge information in the OCT images were well preserved by the proposed algorithm. Meanwhile, the image processing time was reduced by half with the proposed algorithm compared with the traditional K‐SVD algorithm.