标题：Feature-oriented singular value shrinkage for optical coherence tomography image
作者：Chen, Huaiguang; Fu, Shujun; Wang, Hong; Lv, Hongli; Zhang, Caiming; Wang, Fengling; Li, Yuliang
作者机构：[Chen, Huaiguang; Fu, Shujun; Lv, Hongli] Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China.; [Wang, Hong] Univ South Carolina, Dept 更多
通讯作者：Fu, Shujun;Fu, SJ
通讯作者地址：[Fu, SJ]Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China.
来源：OPTICS AND LASERS IN ENGINEERING
关键词：Optical coherence tomography; Speckle; Singular value shrinkage; Mixed; threshold; Adaptive backward projection; Low-rank
摘要：Optical coherence tomography (OCT) is a non-invasive optical imaging modality that has been widely used in the field of medical diagnosis. However, OCT images are often degraded by speckle noise. To address this problem, this paper proposes a two-stage feature-oriented singular value shrinkage algorithm in a low-rank approximation framework, for speckle noise reduction and contrast enhancement of infra-retinal layers of OCT images. First, a weighted absolute distance is employed to find nonlocal similar patches that exhibit high correlation to a given reference one. Next, the singular values of the group matrix formed by similar patches are shrunk by mixed thresholding so that they are closer to the singular values of that of latent noise-free image. Finally, an iterative regularization technique is adopted to improve the denoising performance of the proposed method, where the backward projection parameter in each pixel is adaptively determined by its corresponding gradient variation. By using these strategies, the proposed method not only effectively removes the speckle noise of OCT images, but also preserves fine structural information of objects. Experimental results show that the proposed algorithm is competitive with some state-of-the-art speckle removal techniques in terms of both objective metrics and subjective visual inspection.