Speckle attenuation for optical coherence tomography images using the generalized low rank approximations of matrices

A frequently used technology in medical diagnosis is optical coherence tomography (OCT). However, coherent noise, also known as speckle noise, has the potential to severely reduce the quality of OCT images, which would be detrimental to the use of OCT images for disease diagnosis. In this paper, a despeckling method is proposed to effectively reduce the speckle noise in OCT images using the generalized low rank approximations of matrices (GLRAM). Specifically, the Manhattan distance (MD)-based block matching method is first used to find nonlocal similar blocks for the reference one. The left and right projection matrices shared by these image blocks are then found using the GLRAM approach, and an adaptive method based on asymptotic matrix reconstruction is proposed to determine how many eigenvectors are present in the left and right projection matrices. Finally, all the reconstructed image blocks are aggregated to create the despeckled OCT image. In addition, an edge-guided adaptive back-projection strategy is used to improve the despeckling performance of the proposed method. Experiments with synthetic and real OCT images show that the presented method performs well in both objective measurements and visual evaluation.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

In this paper, a despeckling method using generalized low rank approximations of matrices (GLRAM) is proposed to effectively reduce speckle noise in optical coherence tomography (OCT) images. Nonlocal similar blocks are found using the Manhattan distance (MD)-based block matching method, and the left and right projection matrices shared by these blocks are determined using the GLRAM approach. An adaptive method based on asymptotic matrix reconstruction is used to determine the number of eigenvectors present in the projection matrices, and the despeckled OCT image is created by aggregating all the reconstructed image blocks. The proposed method also utilizes an edge-guided adaptive back-projection strategy to improve despeckling performance.